Compressed air energy storage optimal configuration method and system supporting new energy consumption

Abstract

The invention relates to a compressed air energy storage optimization configuration method and system supporting new energy consumption, and belongs to the field of power system optimization scheduling. According to the method, CAES operation constraints are constructed based on composition of a compressed air energy storage CAES power station, and CAES standby constraints are constructed based on multiple working modes of the CAES power station and upper and lower adjustable standby quantities corresponding to the modes; constructing a CAES two-stage robust planning model by taking the minimum total cost of the CAES power station construction cost and the typical daily power grid operation cost as a target; and solving the two-stage robust planning model based on a CAES operation constraint, a CAES standby constraint, a CAES energy-standby constraint, a CAES construction planning constraint and a typical daily operation constraint to obtain an optimal configuration scheme and an operation scheme of the CAES power station. According to the method, the two-stage robust planning model of the CAES system considering the new energy uncertainty is constructed by considering the rapid conversion of the CAES operation mode, and the flexible adjustment capability of the CAES is effectively mined.

Description

Technical Field

[0001] The present invention belongs to the field of power system optimization and dispatching, and in particular relates to a compressed air energy storage optimization configuration method and system for supporting new energy consumption. Background Art

[0002] With the vigorous development of renewable energy power generation, the strong random volatility of renewable energy power generation has brought challenges to the real-time power balance of the power system. In order to realize the large-scale consumption of renewable energy, the power system needs to have sufficient flexible adjustment resources. With the gradual retirement of coal-fired power, hydropower and gas-fired power are limited by water resources and gas sources respectively, and traditional resources are difficult to meet the regulation needs of the new power system. Therefore, it is necessary to explore new flexible adjustment resources. Large-scale energy storage technology is considered to be an effective solution to adapt to the development of large-scale renewable energy due to its fast charging and discharging capabilities. Among the many energy storage technologies, pumped storage (PHS), compressed air energy storage (CAES) and battery energy storage (BES) are relatively mature. Although PHS has a large adjustment capacity, its development is limited due to site selection restrictions. In contrast, CAES is regarded as an ideal alternative to PHS due to its large capacity, long life and few site selection restrictions.

[0003] Existing research focuses on wind power coupling with traditional CAES systems. It proposes a CAES system capacity optimization configuration method that aims to improve the operating benefits of the combined system, and calculates the overall performance of the CAES system. In addition, there are studies that consider market elements, establish an evaluation model with the goal of minimizing power outage compensation and investment costs, and use dynamic programming to optimize the CAES storage scale. However, the shortcoming of the above research is that it does not consider the uncertainty of wind power generation. In addition, there is a capacity optimization study on the functional cost of CAES power stations in cogeneration systems, and a capacity optimization method for dual-mode CAES power stations is proposed to solve the problem of wind abandonment in island wind farms. The above research plans the compressed air energy storage system under the premise of considering the uncertainty of new energy output, but does not consider the ability of the CAES power station's spare capacity to cope with uncertainty. Summary of the invention

[0004] In view of the above analysis, the present invention aims to provide a compressed air energy storage optimization configuration method and system to support the consumption of new energy. By considering the conversion relationship of CAES operation modes, CAES backup constraints are constructed, and the CAES two-stage robust planning model is solved to obtain the optimal configuration plan and operation plan of the CAES power station, which effectively explores the flexible adjustment ability of CAES backup capacity to cope with the uncertainty of new energy in the power grid.

[0005] On the one hand, the present invention provides a compressed air energy storage optimization configuration method for supporting new energy consumption, which specifically includes the following steps:

[0006] The CAES operation constraints are constructed based on the composition of the compressed air energy storage CAES power station, and the CAES reserve constraints are constructed based on the multiple working modes of the CAES power station and the upper and lower adjustable reserve amounts corresponding to each mode;

[0007] A two-stage robust planning model for CAES is constructed with the goal of minimizing the total cost of CAES power station construction cost and typical daily grid operation cost.

[0008] The two-stage robust programming model is solved based on CAES operation constraints, CAES reserve constraints, CAES energy-reserve constraints, CAES construction planning constraints, and typical day operation constraints to obtain an optimal configuration plan and operation plan for the CAES power station.

[0009] Furthermore, a CAES power station construction cost model is constructed based on the planned compressor power, expander power, gas storage chamber volume, and heat storage tank capacity of each CAES power station;

[0010] A typical day grid operation cost model is constructed based on the output power of each conventional unit in the grid and the adjustable reserve capacity;

[0011] A two-stage CAES robust planning model is constructed based on the CAES power station construction cost model and the typical daily power grid operation cost model.

[0012] Furthermore, the CAES two-stage robust planning model is expressed as:

[0013] min C total =C invest +C op ;

[0014]

[0015] C op =∑ r π r C op,r ;

[0016]

[0017] Among them, C total is the total cost; C invest The construction cost of CAES; C op is the typical daily grid operation cost; C C and C G are the cost coefficients of the compressor and expander respectively; C ST is the cost coefficient of the gas storage room; C Q is the cost coefficient of the heat storage tank; Plan the compressor power for the mth CAES power station; Plan the expander power for the mth CAES power station; Plan the gas storage volume for the mth CAES power station; Plan the heat storage tank capacity for the mth CAES power station; T life is the useful life of the CAES power station; s is the investment discount rate calculated at an annual interest rate; ∑ c represents the sum of all CAES power stations; π r is the proportion of typical days r in a year; C op,r is the typical daily operating cost; C E is the power generation cost of conventional units in the power grid; C R is the standby cost of conventional units in the power grid; the subscript k represents the segment number after the objective function is piecewise linearized; i represents the conventional unit number; t represents the time; c i.k is the marginal cost of conventional unit i in segment k; is the startup cost of conventional unit i at time t; is the shutdown cost of conventional unit i at time t; P i,k,t is the output power of conventional unit i in segment k; and is the upper and lower standby cost coefficient of conventional unit i, and It is the upper and lower adjustable reserve capacity of conventional unit i.

[0018] Furthermore, the multiple working modes include no-load mode, power generation mode and compression mode; and the construction of CAES standby constraints includes:

[0019] A mode conversion relationship is constructed based on the multiple working modes and the conversion between the modes, which is expressed as:

[0020]

[0021] Among them, v g,t represents the operating status of the CAES power station power generation mode during period t, 1 represents operation and 0 represents shutdown; v c,t Represents the operating status of the CAES power plant in compression mode during period t, 1 represents operation and 0 represents shutdown; and Indicates the working mode conversion relationship of the CAES power station. When the CAES power station switches from no-load mode to power generation mode, When the CAES power station switches from power generation mode to no-load mode, When the CAES power station switches from no-load mode to compression mode, When the CAES power station switches from compression mode to no-load mode,

[0022] Constructing CAES reserve constraints based on the mode conversion relationship and the upper and lower adjustable reserve amounts corresponding to each mode, including adjustable reserve constraints in compression mode, adjustable reserve constraints in no-load mode, and adjustable reserve constraints in power generation mode;

[0023] The adjustable spare constraint in the compression mode is expressed as:

[0024]

[0025] Among them, P c,t is the compression power of the CAES power station during period t; and are the upper and lower limits of the compression rate; and is the upper and lower adjustable reserve of the CAES power station in compression mode;

[0026] The adjustable standby constraint in the no-load mode is expressed as:

[0027]

[0028] in, and The upper and lower limits of the power generation capacity of the CAES power station; and It is the upper and lower adjustable reserve amount of CAES in no-load mode;

[0029] The adjustable reserve constraint under the power generation mode is expressed as:

[0030]

[0031] Among them, P c,t is the power generation of the CAES power station during period t; and It is the upper and lower adjustable reserve capacity of CAES in power generation mode.

[0032] Furthermore, the typical day operation constraints include operation constraints under basic scenarios and operation constraints under uncertain scenarios;

[0033] The operation constraints under the basic scenario include conventional unit operation constraints, node power balance constraints, line flow constraints, wind power output constraints, conventional unit output and reserve constraints, conventional unit minimum start / stop constraints, conventional unit ramp constraints and conventional unit regulation reserve constraints;

[0034] The operation constraints under the uncertain scenario include node power balance constraints, line flow constraints, re-dispatching constraints of conventional units, wind power uncertain output constraints, CAES operation constraints and CAES standby constraints.

[0035] Furthermore, the conventional unit adjustment reserve constraint is expressed as:

[0036]

[0037] in, and They respectively represent the upper and lower adjustable reserve capacity of conventional unit i.

[0038] Furthermore, the wind power uncertain output constraint is expressed as a wind power uncertainty set:

[0039]

[0040]

[0041] Among them, U is the wind power uncertainty output set; Contribute to the uncertainty of wind power; w,t Provide output for wind power forecasting; and The 0-1 auxiliary variable is introduced. When there is a positive prediction error in wind power output Take 1, Take 0, when there is a negative prediction error in wind power output Take 0, Take 1; and are the positive and negative prediction errors of wind power output respectively; α represents the wind power output prediction error coefficient; Γ T and Γ S are the temporal and spatial uncertainty budgets of the uncertainty set.

[0042] Furthermore, the CAES operation constraints constructed based on the structure of the compressed air energy storage, i.e., CAES power station, include:

[0043] Construct compression power and power generation constraints based on the compression unit and power generation unit of the CAES power station;

[0044] Establish air pressure variation constraints based on air storage units;

[0045] Establish heat storage constraints based on thermal energy storage tanks;

[0046] Heat transfer power constraints for the compression and power generation stages are constructed based on heat exchangers and expanders.

[0047] Furthermore, based on the DC&CG algorithm, the two-stage robust planning model is solved to obtain the optimal configuration plan and operation plan of CAES. The optimal configuration plan includes the number of CAES power stations to be built, the planned compressor power of each CAES power station, the planned expander power, the gas storage chamber volume, and the heat storage tank capacity; the operation plan includes the working mode, compression power, and power generation power of each CAES power station at each time on each typical day.

[0048] On the other hand, the present invention also provides a compressed air energy storage optimization configuration system for supporting new energy consumption, including:

[0049] Model building and management module, used to build CAES operation constraints based on the composition of compressed air energy storage CAES power station, build CAES reserve model based on multiple working modes of CAES power station and the corresponding upper and lower adjustable reserve capacity of each mode; build CAES two-stage robust planning model with the goal of minimizing the total cost of CAES power station construction cost and typical daily grid operation cost;

[0050] The model solving model is used to solve the two-stage robust programming model based on CAES operation constraints, CAES backup constraints, CAES quantity-backup constraints, CAES construction planning constraints, and typical day operation constraints to obtain the optimal configuration plan and operation plan of CAES.

[0051] The present invention can achieve at least one of the following beneficial effects:

[0052] By modeling the operation of CAES and the backup modeling of each working mode of CAES, the ability of the CAES power station's backup capacity to cope with the uncertainty of renewable energy power generation is simulated. By combining CAES construction planning and simulating the operation of the typical day of the power grid system, a CAES two-stage robust planning model is constructed considering the uncertainty of renewable energy power generation. The two-stage robust scale model is solved to obtain the optimal configuration plan and operation plan of the CAES power station, which effectively explores the flexible adjustment ability of CAES backup capacity to cope with the uncertainty of renewable energy in the power grid, and can more scientifically guide the construction planning and operation of compressed air energy storage CAES.

[0053] By introducing typical day operation simulation to consider the uncertainty of renewable energy output, guidance for CAES capacity planning is achieved; to address the non-convexity problem of the robust planning model caused by the switching of CAES operation modes, a double-layer iterative column and constraint generation (DC&CG) algorithm is proposed to solve the problem that the robust planning model embedded with typical day operation simulation is difficult to solve.

[0054] Other features and advantages of the present invention will be described in the following description, and some advantages may become apparent from the description, or may be understood by practicing the present invention. The purpose and other advantages of the present invention may be realized and obtained through the contents particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings are only used for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. In the entire drawings, the same reference symbols represent the same components;

[0056] Figure 1 is a flow chart of the method of the present invention;

[0057] Figure 2 The actual wind power output of the three simulation models of the present invention;

[0058] Figure 3 The reserve capacity scheduling of conventional units and CAES in the three simulation models of the present invention. DETAILED DESCRIPTION

[0059] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0060] Method Example 1

[0061] A specific embodiment of the present invention discloses a method for optimizing the configuration of compressed air energy storage to support the consumption of new energy, which specifically includes the following steps:

[0062] Step S01: constructing CAES operation constraints based on the structure of a compressed air energy storage CAES power station, and constructing CAES reserve constraints based on multiple working modes of the CAES power station and upper and lower adjustable reserve amounts corresponding to each mode;

[0063] Step S02, constructing a CAES two-stage robust planning model with the goal of minimizing the total cost of CAES power station construction cost and typical daily power grid operation cost; including: constructing a CAES power station construction cost model based on the planned compressor power, expander planned power, gas storage chamber volume, and heat storage tank capacity of each CAES power station; constructing a typical daily power grid operation cost model based on the output power and upper and lower adjustable reserve of each conventional unit of the power grid; constructing a CAES two-stage robust planning model with the total cost target as the minimum based on the CAES power station construction cost model and the typical daily power grid operation cost model; it should be noted that the conventional units of the power grid in this application refer to the generator sets of traditional power generation methods;

[0064] Step S03, based on CAES operation constraints, CAES backup constraints, CAES energy-backup constraints, CAES construction planning constraints, and typical day operation constraints, the two-stage robust planning model is solved to obtain an optimal configuration plan and operation plan for the CAES power station, including the number of CAES power stations to be built, the planned compressor power of each CAES power station, the planned expander power, the gas storage chamber volume, the heat storage tank capacity, the working mode of each CAES power station, the compressor power and the power generation power.

[0065] In this embodiment, by modeling the operation of CAES and the backup modeling of each working mode of CAES, combined with the CAES construction plan and the operation of the power grid system on a typical day, the CAES two-stage robust planning model considering the uncertainty of wind power is solved, which can effectively tap the flexible adjustment ability of CAES backup capacity to cope with the uncertainty of new energy in the power grid, and can more scientifically guide the construction planning and operation of compressed air energy storage CAES.

[0066] The CAES power plant consists of a compression unit, a power generation unit, an air storage unit, a thermal energy storage tank, a heat exchanger and an expander.

[0067] Specifically, in step S01, constructing CAES operation constraints based on the structure of a compressed air energy storage, i.e., a CAES power station, includes:

[0068] Construct compression power and power generation constraints based on the compression unit and power generation unit of the CAES power station;

[0069] Establish air pressure variation constraints based on air storage units;

[0070] Establish heat storage constraints based on thermal energy storage tanks;

[0071] Heat transfer power constraints for the compression and power generation stages are constructed based on heat exchangers and expanders.

[0072] Furthermore, the compression power and generation power constraints include:

[0073]

[0074]

[0075] Among them, P c,t and P g,t are respectively the compression power (i.e. the working power of the compressor, regarded as the charging power of the CAES power station) and the power generation power of the CAES power station during period t; m c,t and m g,t are the flow rates flowing into the compressor during period t; γ is the air specific heat ratio; R g is the ideal gas constant; T c,j,inis the air temperature of the jth compressor; c,j is the compression ratio of the j-th compressor; η c and η g are the efficiencies of the compression process and the power generation process respectively; N c and N g are the number of stages of compressor and expander respectively; and The upper and lower limits of compression power; and The upper and lower limits of power generation.

[0076] Furthermore, the air pressure variation constraints include:

[0077]

[0078] Among them, P caes,t is the air pressure in the air storage chamber during period t; T st,in is the inlet temperature of the air storage chamber; is the initial temperature of the gas storage chamber; V caes is the volume of the air storage chamber; b HT and K HT is the equivalent heat transfer coefficient; T wall is the temperature of the air storage chamber wall; and p caes The upper and lower limits of the air pressure in the air storage chamber; is the initial air pressure in the air storage chamber.

[0079] Furthermore, the heat storage constraints include:

[0080] Q caes,t =Q caes,t-1 +H c,t -H g,t ;

[0081]

[0082] Among them, Q caes,t is the heat in the heat storage tank during period t; H c,t and H g,t are the heat absorption and heat transfer of the heat exchanger in period t respectively; is the upper limit of the heat storage capacity of the heat storage tank; T is the total scheduling time; t0 is the initial time.

[0083] Furthermore, the heat transfer power constraints for the compression stage and the power generation stage include:

[0084]

[0085] Among them, c p,air is the isobaric specific heat capacity of air; ε is the energy efficiency coefficient of the heat exchanger; Tc,j,in and T g,j,in are the air temperatures of the jth stage compressor and expander respectively; T cold is the temperature of the cold heat medium; is the initial temperature of the heat transfer medium.

[0086] Furthermore, the CAES operation constraints also include CAES operation condition constraints, which are expressed as:

[0087] v c,t +v g,t ≤1,v c,t ,v g,t ∈{0,1};

[0088] Among them, v g,t represents the operating status of the CAES power station power generation mode during period t, 1 represents operation and 0 represents shutdown; v c,t Represents the operating status of the CAES power plant in compression mode during period t, 1 represents operation and 0 represents shutdown.

[0089] Specifically, in step S01 , the multiple working modes of the CAES power station include a no-load mode, a power generation mode and a compression mode.

[0090] The CAES reserve constraints are constructed based on multiple working modes of the CAES power station and the upper and lower adjustable reserve amounts corresponding to each mode, including:

[0091] A mode conversion relationship is constructed based on the multiple working modes and the conversion between the modes, which is expressed as:

[0092]

[0093] in, and Indicates the working mode conversion relationship of the CAES power station. When the CAES power station switches from no-load mode to power generation mode, When the CAES power station switches from power generation mode to no-load mode, When the CAES power station switches from no-load mode to compression mode, When the CAES power station switches from compression mode to no-load mode,

[0094] Constructing CAES reserve constraints based on the mode conversion relationship and the upper and lower adjustable reserve amounts corresponding to each mode, including adjustable reserve constraints in compression mode, adjustable reserve constraints in no-load mode, and adjustable reserve constraints in power generation mode;

[0095] The adjustable spare constraint in the compression mode is expressed as:

[0096]

[0097] Among them, P c,t is the compression power of the CAES power station during period t; and The upper and lower limits of compression power; and is the upper and lower adjustable reserve of the CAES power station in compression mode;

[0098] The adjustable standby constraint in the no-load mode is expressed as:

[0099]

[0100] in, and The upper and lower limits of the power generation capacity of the CAES power station; and It is the upper and lower adjustable reserve amount of CAES in no-load mode;

[0101] The adjustable reserve constraint under the power generation mode is expressed as:

[0102]

[0103] Among them, P c,t is the power generation of the CAES power station during period t; and It is the upper and lower adjustable reserve capacity of CAES in power generation mode.

[0104] It should be noted that, on the basis of constructing CAES operation constraints and CAES reserve constraints, it is also necessary to consider ensuring the reserve availability of CAES. This is because when the air pressure or thermal storage is close to the upper or lower limit, the compression or power generation capacity of CAES will be limited and it will not be able to provide reserve auxiliary services. Therefore, further, CAES energy-reserve constraints are constructed to ensure the deliverability of reserve capacity.

[0105] Specifically, the CAES energy-reserve constraint includes constraint equations (1) to (14):

[0106]

[0107] in, and p caes The upper and lower limits of the air pressure in the air storage chamber; and is the minimum and maximum air pressure levels at time t; P caes,t is the air pressure in the air storage chamber during time period t; Equations (1) to (3) describe the minimum air pressure level at time t and the maximum air pressure level at time t Subject to the corresponding upper and lower pressure limits and the air pressure constraints under normal conditions;

[0108]

[0109]

[0110] Among them, T st,in is the inlet temperature of the air storage chamber; V caes is the volume of the air storage chamber; f c and f g is m c,t and m g,t Relative to P c,t and P g,t Function of and is the upper and lower adjustable reserve of CAES in compression mode; b HT and K HT is the equivalent heat transfer coefficient; is the initial temperature of the gas storage chamber; T wall is the temperature of the air storage chamber wall; and It is the upper and lower adjustable reserve of CAES in power generation mode; and It is the upper and lower adjustable reserve amount of CAES in no-load mode;

[0111] Equations (4)-(5) simulate the situation in which the CAES power station provides backup power in the compression / generation mode. When CAES is dispatched to generate electricity and provide upward reserve at time t, the minimum air pressure level at time t is From the previous moment Determined by subtracting the maximum pressure drop at time t, which corresponds to the maximum power generation capacity Conversely, when CAES is dispatched at time t for compression and to provide updraft reserve, the minimum pressure level at time t is Equal to the previous moment Plus the minimum pressure rise at time t; Similarly, equations (6)-(7) simulate the CAES power station providing backup power in the compression / generation mode. The impact of

[0112]

[0113] in, is the upper limit of heat storage; and are the minimum heat storage level and the maximum heat storage level at time t respectively; Equations (8) to (10) describe the minimum heat storage level and maximum heat storage level Subject to the corresponding upper and lower thermal storage limits and thermal storage under normal conditions;

[0114]

[0115] Among them, h c and h g Yes H c,t and H g,t Relative to P c,t and P g,t function; Equations (11) to (12) describe the CAES power station providing backup power in the compression / generation mode. When CAES is dispatched to generate electricity and provide upward reserve at time t, the minimum thermal storage level at time t is From the previous moment minus the maximum thermal storage reduction at time t, which corresponds to the maximum power generation capacity Conversely, when CAES is dispatched for compression and provides up-regulation reserve at time t, the minimum thermal storage limit at time t is equal to the previous period Plus the minimum thermal storage increase at time t; Similarly, equations (13) to (14) describe the CAES power station providing backup power in the compression / generation mode. impact.

[0116] Specifically, in step S02, the uncertainty of new energy is taken into consideration. In this application, the uncertainty of wind power is mainly considered to construct a CAES two-stage robust planning model.

[0117] Specifically, a CAES power station construction cost model is constructed based on the planned compressor power, expander power, gas storage chamber volume, and heat storage tank capacity of each CAES power station;

[0118] A typical day grid operation cost model is constructed based on the output power of each conventional unit in the grid and the adjustable reserve capacity;

[0119] A two-stage CAES robust planning model is constructed based on the CAES power station construction cost model and the typical daily power grid operation cost model.

[0120] Specifically, the CAES two-stage robust planning model is expressed as:

[0121] Objective function: minC total =C invest +C op ;

[0122] CAES power station construction cost model:

[0123]

[0124] Typical daily grid operation cost model:

[0125] C op =∑ r π r C op,r ;

[0126]

[0127] Among them, C total is the total cost; C invest C is the construction cost of CAES; op is the typical daily grid operation cost; C C and C G are the cost coefficients of the compressor and expander respectively; C ST is the cost coefficient of the gas storage room; C Q is the cost coefficient of the heat storage tank; Plan the compressor power for the mth CAES power station; Plan the expander power for the mth CAES power station; Plan the gas storage volume for the mth CAES power station; Plan the heat storage tank capacity for the mth CAES power station; T life is the useful life of the CAES power station; s is the investment discount rate calculated at an annual interest rate; ∑ c represents the sum of all CAES power stations; π r is the proportion of typical days r in a year; C op,r is the typical daily operating cost; C E is the power generation cost of conventional units in the power grid; C R is the standby cost of conventional units in the power grid; the subscript k represents the segment number after the objective function is piecewise linearized; i represents the conventional unit number; t represents the time; c i.k is the marginal cost of conventional unit i in segment k; is the startup cost of conventional unit i at time t; is the shutdown cost of conventional unit i at time t; P i,k,t is the output power of conventional unit i in segment k; and is the upper and lower standby cost coefficient of conventional unit i, and It is the upper and lower adjustable reserve capacity of conventional unit i.

[0128] Specifically, in step S03, the CAES construction planning constraints include construction quantity constraints, planning power constraints, planning capacity constraints and construction cost constraints.

[0129] Furthermore, the construction quantity constraint refers to the quantity constraint on the CAES power stations to be constructed.

[0130] Furthermore, the planning power constraint is expressed as:

[0131]

[0132] in, and They are the upper and lower limits of the planned power of the compressor respectively; To plan the power of the compressor; and They are the upper and lower limits of the planned power of the expander respectively; To plan the power of the expander.

[0133] Furthermore, the planning capacity constraint is expressed as:

[0134]

[0135] in, and are the minimum and maximum volume of the air storage chamber respectively; Q min and Q max are the minimum and maximum values ​​of the heat storage tank capacity respectively; Planning the volume of gas storage chamber for CAES power station; Planning thermal storage tank capacity for CAES power plants.

[0136] Furthermore, the construction cost constraint is expressed as:

[0137]

[0138] Among them, Π is the upper limit of the construction cost budget.

[0139] Specifically, in step S03, the typical day operation constraints include operation constraints under basic scenarios and operation constraints under uncertain scenarios;

[0140] The operation constraints under the basic scenario include conventional unit operation constraints, node power balance constraints, line flow constraints, wind power output constraints, conventional unit output and reserve constraints, conventional unit minimum start / stop constraints, conventional unit ramp constraints and conventional unit regulation reserve constraints;

[0141] The operation constraints under the uncertain scenario include node power balance constraints, line flow constraints, re-dispatching constraints of conventional units, wind power uncertain output constraints, CAES operation constraints and CAES standby constraints.

[0142] Furthermore, the node power balance constraint is expressed as:

[0143]

[0144] Where G(m) is the set of conventional units connected to the grid node m; W(m) is the set of wind turbines connected to the node m; CAES(m) is the set of CAES power stations connected to the node m; L(m) is the set of transmission lines connected to the node m; D(m) is the set of loads connected to the node m; P i,t is the output power of unit i at time t; P w,t is the actual output power of the wind turbine at time t; P l,t is the power flowing through the transmission line l at time t; P d,t is the load power forecast value at time t.

[0145] Furthermore, the line power flow constraint is expressed as:

[0146] P l,t =(θ m,t -θ n,t ) / x l ,θ ref =0;

[0147] -P l max ≤P l,t ≤P l max ;

[0148] Among them, θ m,t and θ n,t is the phase angle between the first and last nodes of the transmission line l at time t; θ ref is the reference node phase angle value; x l is the reactance value of the transmission line l; P l max It is the maximum power allowed to flow through the transmission line l.

[0149] Furthermore, the wind power output constraint is expressed as:

[0150]

[0151] in, is the predicted output power of the wind turbine at time t.

[0152] Furthermore, the output and reserve constraints of conventional units are expressed as:

[0153] P i,t =∑ k P i,k,t ;

[0154]

[0155] Among them, P i,tis the output power of conventional unit i; P i,k,t is the output power of conventional unit i in segment k; P i max and P i min is the upper and lower limits of the output power of conventional unit i; I i,t Indicates the normal unit operating status (1 for startup, 0 for shutdown); and Respectively represent the upper and lower adjustable reserve capacity of conventional unit i and It is the maximum value of the adjustable reserve capacity of conventional unit i.

[0156] It should be noted that in the power grid system, if the reserve demand is fully borne by CAES, an unadjustable area may appear, resulting in the inability to completely suppress the system power imbalance. Therefore, a certain amount of reserve needs to be purchased from traditional units to ensure continuous adjustment of the reserve. In this case, the reserve of traditional units should meet the reserve adjustment constraints of conventional units to cover the unadjustable area.

[0157] Furthermore, the conventional unit regulation reserve constraint is expressed as:

[0158]

[0159] It should be noted that conventional unit operation constraints, conventional unit minimum start / shutdown constraints, and conventional unit climbing constraints are common knowledge in the field and will not be elaborated here one by one.

[0160] Similarly, the node power balance constraints, line flow constraints, and re-dispatching constraints of conventional units under uncertain scenarios will not be elaborated one by one.

[0161] Furthermore, the wind power uncertain output constraint is expressed as a wind power uncertainty set:

[0162]

[0163] Among them, U is the wind power uncertainty output set; Contribute to the uncertainty of wind power; w,t Provide output for wind power forecasting; and The 0-1 auxiliary variable is introduced. When there is a positive prediction error in wind power output Take 1, Take 0, when there is a negative prediction error in wind power output Take 0, Take 1; and are the positive and negative prediction errors of wind power output respectively; α represents the wind power output prediction error coefficient; ΓT and Γ S are the temporal and spatial uncertainty budgets of the uncertainty set.

[0164] Specifically, in step S03, the CAES two-stage robust programming model is solved based on a double-layer iterative column and constraint generation algorithm (DC&CG). It should be noted that since CAES meets the safety constraints under uncertain events by switching working modes, 0-1 variables are introduced, making it impossible for the C&CG algorithm to solve the CAES two-stage robust programming model. Therefore, the present invention proposes a double-layer iterative column and constraint generation algorithm to solve the CAES two-stage robust programming model.

[0165] Specifically, in order to simplify the analysis and facilitate the solution, the CAES two-stage robust planning model is rewritten into the following compact model:

[0166]

[0167] Wherein, the superscript b represents the expected scenario, the superscript u represents the uncertain scenario; I represents the start and stop status of the conventional unit; p represents the output of the conventional unit; θ represents the node phase angle; pw represents the wind power output; v represents the operating status of the CAES power station; p caes Indicates the power generation or charging power of the CAES power station; h caes Indicates the heat absorption and heat transfer of the heat exchanger; pst caes Indicates the air pressure in the air storage chamber; q caes Indicates the heat in the heat storage tank; m caes Indicates the flow into the compressor and expander; Indicates the planned power of the CAES power station; V plan represents the planned gas storage chamber volume; q plan Plan the capacity of the heat storage tank; s represents the operating loss under the uncertain scenario; pwu represents the wind power uncertainty scenario; a, b, c, f, g, l, A, B, C, K, Q, N, L, J, E, F, H, G, Z, W, R, X, S are the corresponding coefficient matrices.

[0168] Furthermore, there are max-min constraints in equation (15), which cannot be solved directly. Therefore, the original problem is decomposed into the main planning-operation problem under the expected scenario and the safety verification subproblem under the uncertain scenario. For the infeasible scenario in the feasibility verification subproblem, the optimal cutting plane is added to the first stage in the form of the original problem.

[0169] Furthermore, the goal under the basic scenario is shown in the objective function. The constraints mainly include the CAES construction planning constraints and the operation constraints under the basic scenario, as well as the C&CG optimal cutting plane returning to the first stage under the infeasible scenario of the second stage (i.e., the worst wind power output scenario).

[0170]

[0171] Furthermore, equation (16) is a mixed integer nonlinear programming (MINLP) problem, which is solved by calling the OPTI toolbox, the commercial solver BARON or the heuristic algorithm. The CAES power station planning decision variables are obtained by solving equation (16): V plan ,q plan , and the scheduling decision variable I under a typical day b 、p b 、pw、v b ,

[0172] Furthermore, the safety check sub-problem explores the possible operational losses that the uncertainty of wind power output on a typical day may cause to the system. worst If the maximum operating loss under the condition exceeds the predefined threshold, a feasible C&CG cut plane is generated to return to the main problem, and the main problem is re-solved to obtain a new planning-scheduling solution to alleviate the operating loss. If the maximum operating loss is less than the predefined threshold, the optimal planning-scheduling solution is obtained through safety verification.

[0173] Furthermore, for the safety verification subproblem, the worst scenario of wind power output on a typical day is first determined. The safety verification subproblem under uncertain scenarios is shown in formula (17):

[0174]

[0175] Formula (17) is a max-min two-level optimization problem. When the inner min problem is a linear programming (LP) problem, the dual transformation can be used to transform Formula (17) into a single-level max programming problem. However, Formula (17) contains the operating state variable vu of the CAES power station, so the inner min problem is a MILP problem and the dual transformation cannot be directly applied. Therefore, the inner safety check subproblem is solved by the C&CG method.

[0176] Further, the inner C&CG algorithm is described as follows (including steps 1 to 3):

[0177] Step 1. Set the upper bound ub = +∞ and the lower bound lb = -∞ of the problem in equation (17); set the number of iterations r = 1, and give the initialization value At a given initial value The inner min problem becomes an LP problem, which can be converted into a max problem according to the weak duality theorem.

[0178]

[0179] Step 2. In equation (18), λ represents the dual variable of the constraint in equation (18). There is a nonlinear term λ in the objective of equation (18). T pwu, but at the same time pwu is a function of zw (column vector), so λ T G·pwu is converted into λ T P·zw, and convert it into a linear term using the additional variable method to obtain the worst scenario pwu worst and the maximum running loss. Update ub = ψ if (ψ represents the upper bound of the C&CG algorithm, ε represents the convergence threshold of the algorithm), terminate the iteration, and derive the worst scenario pwu worst , otherwise go to step 3.

[0180] Step 3. Put pwu worst Import into problem (19), solve the MILP problem, and update lb = max{lb,γ}; if Terminate the iteration and export the worst-case scenario pwu worst ; Otherwise, export (r represents the number of iterations), and create a new variable λ (r+1) , return to step 1 and solve equation (18) again.

[0181]

[0182] Then generate the C&CG optimal cutting plane corresponding to the worst scenario. After solving equation (17), if the wind abandonment and load shedding in the worst scenario are greater than 0, the optimal solution of λ and zw is λ (r+1) 、zw (r+1) , and add a new variable s to the main problem (r+1) 、p u(r+1) ,θ u(r+1) 、v u(r+1) , Generate the C&CG optimal cutting plane (20) and return to the UC main problem.

[0183]

[0184] Finally, the two-stage robust planning model is solved based on the DC&CG algorithm to obtain the optimal configuration plan and operation plan of CAES. The optimal configuration plan includes the number of CAES power stations to be built, the planned compression power of each CAES power station, the planned expander power, the volume of the gas storage chamber, and the capacity of the heat storage tank; the operation plan includes the working mode, compression power and power generation power of each CAES power station at each time on each typical day.

[0185] This embodiment discloses a compressed air energy storage optimization configuration method for supporting the consumption of new energy. By modeling the operation of CAES and the backup modeling of each working mode of CAES, combined with the CAES construction plan and the operation of the power grid system on a typical day, a two-stage robust planning model of CAES considering the uncertainty of wind power is solved, and the flexible adjustment capability of CAES backup capacity to cope with the uncertainty of new energy in the power grid is effectively explored, which can provide more scientific guidance for the construction planning and operation of compressed air energy storage CAES.

[0186] By considering the uncertainty of renewable energy output in typical day operation simulation, guidance for CAES capacity planning is achieved; finally, to address the non-convexity problem of the robust planning model caused by the switching of CAES operation modes, a two-layer iterative column and constraint generation (DC&CG) algorithm is proposed to solve the problem that the robust planning model embedded in the typical day operation simulation is difficult to solve.

[0187] Method Example 2

[0188] A specific embodiment of the present invention discloses simulation calculation results of a compressed air energy storage optimization configuration method for supporting new energy consumption.

[0189] Specifically, the simulated power grid is set to include three conventional units and one wind farm. The total dispatching time is 24 hours (day before), and each time period is 1 hour. The wind power uncertainty set parameters are set as follows: the prediction error coefficient α is taken as 0.3, Г T Take it as 12.

[0190] Furthermore, simulations were performed according to three different grid configuration models, as shown in Table 1. The RRD model does not include CAES power stations, and conventional units are mainly used to provide backup; the SERA1 model includes CAES power stations, and fixed working mode (FW) is used to provide backup capacity; the SERA2 model includes CAES power stations, and switching working mode (SW) is used to provide backup auxiliary services.

[0191] Table 1 Comparison of simulation models

[0192]

[0193] Furthermore, the planning results and costs of the three simulation models are shown in Table 2.

[0194] Table 2 Comparison of operating costs of three models

[0195]

[0196] Specifically, it can be seen from Table 2 that the planned capacity without considering the rapid switching of CAES working mode is 35MW, and the planning result considering the rapid switching working mode of CAES is reduced to 25MW. It can be seen that considering the operation of CAES rapid switching working mode can tap its regulation ability and reduce the demand for capacity configuration. Furthermore, it can be seen from Table 2 that the planning including CAES power stations reduces the total cost of the system. This is because the configuration of CAES increases the absorption rate of new energy and reduces the operating cost of the system. Furthermore, it can also be seen that compared with the SERA1 model, in the SERA2 model, CAES adopts a switching working mode to provide backup, which reduces the total cost by 6.4%, that is, operating CAES power stations in a rapid switching working mode can further reduce the total cost.

[0197] Specifically, attached Figure 2 The actual wind power output of the three simulation models is shown. Compared with the RRD model, the participation of CAES in the system power and reserve scheduling in the SERA2 model increases the wind power consumption rate by 9.1%.

[0198] Specifically, attached Figure 3 The reserve capacity dispatch of conventional units and CAES in three simulation models is shown. Figure 3 a) has the lowest reserve capacity requirement, because the RRD model has the least amount of wind power connected to the grid, indicating that the utilization rate of wind power is low. After the system is configured with CAES, the reserve capacity provided by conventional units is reduced. The reserve capacity of CAES power stations effectively provides regulation for the grid, such as Figure 3 (b) and (c) further demonstrate that CAES can provide backup capacity by adopting a more flexible SW mode.

[0199] System Example

[0200] A compressed air energy storage optimization configuration system supporting new energy consumption, including a model building and management module and a model solving model;

[0201] Model building and management module, used to build CAES operation constraints based on the composition of compressed air energy storage CAES power station, build CAES reserve model based on multiple working modes of CAES power station and the corresponding upper and lower adjustable reserve capacity of each mode; build CAES two-stage robust planning model with the goal of minimizing the total cost of CAES power station construction cost and typical daily grid operation cost;

[0202] The model solving model is used to solve the two-stage robust programming model based on CAES operation constraints, CAES backup constraints, CAES quantity-backup constraints, CAES construction planning constraints, and typical day operation constraints to obtain the optimal configuration plan and operation plan of CAES.

[0203] Compared with the prior art, the beneficial effects of the compressed air energy storage optimization configuration system for supporting new energy consumption provided by this embodiment are basically the same as the beneficial effects provided by the method embodiment, and will not be elaborated here one by one.

[0204] It should be noted that the above embodiments are based on the same inventive concept and parts not described repeatedly can be used as reference for each other.

[0205] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for optimizing the configuration of compressed air energy storage to support the consumption of new energy, characterized in that: The steps include: The CAES operation constraints are constructed based on the composition of the compressed air energy storage CAES power station, and the CAES reserve constraints are constructed based on the multiple working modes of the CAES power station and the upper and lower adjustable reserve amounts corresponding to each mode; A two-stage robust planning model for CAES is constructed with the goal of minimizing the total cost of CAES power station construction cost and typical daily grid operation cost. The two-stage robust programming model is solved based on CAES operation constraints, CAES reserve constraints, CAES energy-reserve constraints, CAES construction planning constraints, and typical day operation constraints to obtain an optimal configuration plan and operation plan for the CAES power station.

2. The optimization configuration method according to claim 1, characterized in that: The CAES power station construction cost model is constructed based on the planned compressor power, expander power, gas storage chamber volume, and heat storage tank capacity of each CAES power station; A typical day grid operation cost model is constructed based on the output power of each conventional unit in the grid and the adjustable reserve capacity; A two-stage CAES robust planning model is constructed based on the CAES power station construction cost model and the typical daily power grid operation cost model.

3. The optimization configuration method according to claim 2, characterized in that: The CAES two-stage robust planning model is expressed as: minC total =C invest +C op ; C op =∑ r π r C op,r ; Among them, C total is the total cost; C invest The construction cost of CAES; C op is the typical daily grid operation cost; C C and C G are the cost coefficients of the compressor and expander respectively; C ST is the cost coefficient of the gas storage room; C Q is the cost coefficient of the heat storage tank; Plan the compressor power for the mth CAES power station; Plan the expander power for the mth CAES power station; Plan the gas storage volume for the mth CAES power station; Plan the heat storage tank capacity for the mth CAES power station; T life is the useful life of the CAES power station; s is the investment discount rate calculated at an annual interest rate; ∑ c represents the sum of all CAES power stations; π r is the proportion of typical days r in a year; C op,r is the typical daily operating cost; C E is the power generation cost of conventional units in the power grid; C R is the standby cost of conventional units in the power grid; the subscript k represents the segment number after the objective function is piecewise linearized; i represents the conventional unit number; t represents the time; c i.k is the marginal cost of conventional unit i in segment k; is the startup cost of conventional unit i at time t; is the shutdown cost of conventional unit i at time t; P i,k,t is the output power of conventional unit i in segment k; and is the upper and lower standby cost coefficient of conventional unit i, and It is the upper and lower adjustable reserve capacity of conventional unit i.

4. The optimization configuration method according to claim 3, characterized in that: The multiple working modes include no-load mode, power generation mode and compression mode; and the constructing of CAES standby constraints includes: A mode conversion relationship is constructed based on the multiple working modes and the conversion between the modes, which is expressed as: Among them, v g,t represents the operating status of the CAES power station power generation mode during period t, 1 represents operation and 0 represents shutdown; v c,t Represents the operating status of the CAES power plant in compression mode during period t, 1 represents operation and 0 represents shutdown; and Indicates the working mode conversion relationship of the CAES power station. When the CAES power station switches from no-load mode to power generation mode, When the CAES power station switches from power generation mode to no-load mode, When the CAES power station switches from no-load mode to compression mode, When the CAES power station switches from compression mode to no-load mode, Constructing CAES reserve constraints based on the mode conversion relationship and the upper and lower adjustable reserve amounts corresponding to each mode, including adjustable reserve constraints in compression mode, adjustable reserve constraints in no-load mode, and adjustable reserve constraints in power generation mode; The adjustable spare constraint in the compression mode is expressed as: Among them, P c,t is the compression power of the CAES power station during period t; and The upper and lower limits of compression power; and is the upper and lower adjustable reserve of the CAES power station in compression mode; The adjustable standby constraint in the no-load mode is expressed as: in, and The upper and lower limits of the power generation capacity of the CAES power station; and It is the upper and lower adjustable reserve amount of CAES in no-load mode; The adjustable reserve constraint under the power generation mode is expressed as: Among them, P c,t is the power generation of the CAES power station during period t; and It is the upper and lower adjustable reserve capacity of CAES in power generation mode.

5. The optimization configuration method according to claim 4, characterized in that: The typical day operation constraints include operation constraints under basic scenarios and operation constraints under uncertain scenarios; The operation constraints under the basic scenario include conventional unit operation constraints, node power balance constraints, line flow constraints, wind power output constraints, conventional unit output and reserve constraints, conventional unit minimum start / stop constraints, conventional unit ramp constraints and conventional unit regulation reserve constraints; The operation constraints under the uncertain scenario include node power balance constraints, line flow constraints, re-dispatching constraints of conventional units, wind power uncertain output constraints, CAES operation constraints and CAES standby constraints.

6. The optimization configuration method according to claim 5, characterized in that: The conventional unit regulation reserve constraint is expressed as: in, and They respectively represent the upper and lower adjustable reserve capacity of conventional unit i.

7. The optimization configuration method according to claim 5, characterized in that: The wind power uncertain output constraint is expressed as a wind power uncertainty set: Among them, U is the wind power uncertainty output set; Contribute to the uncertainty of wind power; w,t Provide output for wind power forecasting; and The 0-1 auxiliary variable is introduced. When there is a positive prediction error in wind power output Take 1, Take 0, when there is a negative prediction error in wind power output Take 0, Take 1; and are the positive and negative prediction errors of wind power output respectively; α represents the wind power output prediction error coefficient; Γ T and Γ S are the temporal and spatial uncertainty budgets of the uncertainty set.

8. The optimization configuration method according to claim 5, characterized in that: The CAES operation constraints constructed based on the structure of the compressed air energy storage, i.e., CAES power station, include: Construct compression power and power generation constraints based on the compression unit and power generation unit of the CAES power station; Establish air pressure variation constraints based on air storage units; Establish heat storage constraints based on thermal energy storage tanks; Heat transfer power constraints for the compression and power generation stages are constructed based on heat exchangers and expanders.

9. The optimization configuration method according to claim 5, characterized in that: The two-stage robust planning model is solved based on the DC&CG algorithm to obtain the optimal configuration plan and operation plan of CAES. The optimal configuration plan includes the number of CAES power stations to be built, the planned compression power of each CAES power station, the planned expander power, the volume of the gas storage chamber, and the capacity of the heat storage tank; the operation plan includes the working mode, compression power, and power generation power of each CAES power station at each time on each typical day.

10. A compressed air energy storage optimization configuration system supporting new energy consumption, characterized in that: include: Model building and management module, used to build CAES operation constraints based on the composition of compressed air energy storage CAES power station, build CAES reserve model based on multiple working modes of CAES power station and the corresponding upper and lower adjustable reserve capacity of each mode; build CAES two-stage robust planning model with the goal of minimizing the total cost of CAES power station construction cost and typical daily grid operation cost; The model solving model is used to solve the two-stage robust programming model based on CAES operation constraints, CAES backup constraints, CAES quantity-backup constraints, CAES construction planning constraints, and typical day operation constraints to obtain the optimal configuration plan and operation plan of CAES.

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