A power system scheduling method considering nonlinear variable operating condition characteristics of large-scale advanced compressed air energy storage

Abstract

The application discloses a power system scheduling method considering nonlinear variable working condition characteristics of large-scale advanced compressed air energy storage, and belongs to the field of power system optimization scheduling. When a day-ahead economic scheduling model of a power system is established, the method comprehensively considers the key parameter change characteristics of A-CAES under variable working condition, covers the change characteristics and coupling relationship of parameters such as ambient temperature, isentropic efficiency, compression ratio, expansion ratio, gas storage chamber and hot water tank temperature in the A-CAES operation process, so that the scheduling plan prepared is more in line with the actual operation conditions of A-CAES, and has safety and economy. The method overcomes the shortcomings that the existing linear fixed parameter model and segmented linearization model are excessively optimistic and exaggerated about the performance of A-CAES. Furthermore, the method realizes fast and accurate convergence of optimization by using an iterative solution method, and realizes efficient and accurate solution of the scheduling model.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of power system optimal dispatching, and more particularly to a power system dispatching method considering nonlinear variable working condition characteristics of large-scale advanced compressed air energy storage. BACKGROUND

[0002] Promoting the large-scale development of clean energy such as wind and light is a key measure to achieve carbon neutralization and build a modern energy system. However, its inherent uncertainty characteristics make the safe and stable operation of the power system face severe challenges. In this context, advanced compressed air energy storage (A-CAES) has emerged and is highly expected due to its small investment, environmental friendliness, and flexible site selection.

[0003] A-CAES is a mechanical energy storage that uses compressed air as the working medium. During charging, the compressor works to compress and store air, while the circulating water absorbs the compression heat and stores it. During discharging, high-pressure air is released to drive the expander and drive the generator, while the heated circulating water is used to heat the air to improve the air's work capacity. Throughout the entire process, A-CAES realizes the mutual conversion between electrical energy and compressed air potential energy, and circulating water heat energy.

[0004] To fully realize the value of large-scale energy throughput of A-CAES, an accurate mathematical model and a reasonable power plan are the key. The existing scheduling models of power systems containing A-CAES usually adopt a linear constant parameter model or a piecewise linearization model. However, in A-CAES, most state variables will deteriorate with the reduction of energy storage power, and their optimal working state is the rated state, at which the power is rated. Taking the isentropic compression efficiency of the compression working condition as an example, when A-CAES works at the rated compression power, the isentropic compression efficiency usually reaches the maximum value, and with the reduction of power, the isentropic compression efficiency decreases, and the change of the isentropic compression efficiency with power is nonlinear. Therefore, in order to simplify the calculation difficulty in scheduling research, some studies assume that the isentropic compression efficiency is constant and does not change with power, and a linear constant parameter model is constructed accordingly; some studies consider the change characteristics of the isentropic compression efficiency with power, but fit it as a piecewise linear function, and most of them are 2-3 segments, and the fitting effect is very rough, and a piecewise linearization model is constructed accordingly. The above models more or less exaggerate the isentropic compression efficiency of A-CAES, underestimate the influence of power reduction on the deterioration of the working efficiency of A-CAES, and have the shortcomings of excessive optimism and exaggeration of the performance of A-CAES, so that the scheduling plan prepared does not meet the safety regulations in the actual operation of A-CAES, and if the scheduling instruction is issued to A-CAES, there is a risk of exceeding the limit of the pressure of the gas storage chamber or the mass of the hot water tank of A-CAES. SUMMARY

[0005] In view of the above defects or improvement needs of the prior art, the present application provides a power system scheduling method considering the nonlinear variable working condition characteristics of large-scale advanced compressed air energy storage, which comprehensively covers the change characteristics of the environmental temperature, isentropic efficiency, compression ratio, expansion ratio, gas storage chamber and hot water tank temperature and other parameters during the operation of A-CAES, and can make the scheduling plan prepared more in line with the actual operation conditions of A-CAES.

[0006] To achieve the above object, according to a first aspect of the present application, a power system scheduling method considering the nonlinear variable working condition characteristics of large-scale advanced compressed air energy storage is provided, comprising:

[0007] A day-ahead economic scheduling model of a power system considering the variable working condition characteristics of A-CAES is established with the minimum total operation cost of the power system as the target, and is solved under preset constraints to obtain the optimal scheduling scheme of the generator units and A-CAES in the power system;

[0008] The preset constraints include generator unit constraints, power system constraints and A-CAES constraints; the A-CAES constraints include the state variable operating boundary constraints of A-CAES under each working condition and the analytical expressions of the state variables.

[0009] The power system is a thermal power system, a new energy power system or a hydroelectric power system.

[0010] According to a second aspect of the present application, an electronic device is provided, comprising: a computer readable storage medium and a processor;

[0011] The computer readable storage medium is configured to store executable instructions;

[0012] The processor is configured to read the executable instructions stored in the computer readable storage medium and execute the method according to the first aspect.

[0013] According to a third aspect of the present application, a computer readable storage medium is provided, the computer readable storage medium stores computer instructions, the computer instructions are configured to enable a processor to execute the method according to the first aspect.

[0014] According to a fourth aspect of the present application, a computer program product is provided, comprising computer programs or instructions, the computer programs or instructions are executed by a processor to implement the method according to the first aspect.

[0015] Overall, compared with the prior art, the above technical solutions conceived by the present application can achieve the following beneficial effects:

[0016] 1. The method provided by the present application comprehensively considers the variation characteristics of key parameters of A-CAES under variable working conditions when establishing a day-ahead economic dispatch model of a power system, covers the variation characteristics and coupling relationship of parameters such as ambient temperature, isentropic efficiency, compression ratio, expansion ratio, gas reservoir and hot water tank temperature in the A-CAES operation process, so that the formulated dispatch plan is more in line with the actual operation conditions of A-CAES, has safety and economy, and overcomes the shortcomings of the existing linear fixed parameter model and segmented linearization model that excessively optimistically and exaggeratedly describe the performance of A-CAES.

[0017] 2. Further, for the nonlinearity of the dispatch model, the method provided by the present application proposes an iterative solution method with accuracy and high efficiency for solving the day-ahead economic dispatch problem of a power system. The application of the iterative solution method makes it possible to efficiently and accurately solve the day-ahead economic dispatch model containing a large number of nonlinear factors, fully meeting the timeliness requirements of day-ahead dispatch; the solution efficiency and optimization performance are superior to those of the traditional segmented linearization solution method; in addition, since the nonlinear model of A-CAES is not directly embedded into the dispatch optimization process, the solution efficiency of the iterative solution method is almost not affected by the number of A-CAES and the complexity of the A-CAES model. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 A-CAES structure diagram provided for an embodiment of the present application;

[0019] Figure 2 Isothermal compression efficiency and isothermal expansion efficiency curve provided for an embodiment of the present application;

[0020] Figure 3 System topology diagram provided for an embodiment of the present application;

[0021] Figure 4 Wind power, load power and environmental temperature prediction curve provided for an embodiment of the present application;

[0022] Figure 5 A-CAES power schematic diagram of Case 1 and Case 2 provided for an embodiment of the present application;

[0023] Figure 6 A-CAES gas storage chamber pressure schematic diagram of Case 1 and Case 2 provided for an embodiment of the present application;

[0024] Figure 7 A-CAES hot water tank mass schematic diagram of Case 1 and Case 2 provided for an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0026] The embodiment of the present application provides a power system dispatching method considering the nonlinear variable working condition characteristics of large-scale advanced compressed air energy storage, comprising:

[0027] A power system day-ahead economic dispatching model considering the variable working condition characteristics of A-CAES is established with the minimum total operation cost of the power system as the target, and the optimal dispatching scheme of the generator set and A-CAES in the power system is obtained by solving under preset constraints;

[0028] The preset constraints include generator set constraints, power system constraints and A-CAES constraints; the A-CAES constraints include state variable operating boundary constraints of A-CAES under each working condition and the analytical expression of the state variable;

[0029] The power system is a thermal power generation system, a new energy (wind power, photovoltaic) power generation system or a hydroelectric power generation system.

[0030] Firstly, the A-CAES nonlinear thermodynamic mathematical model covering the variation characteristics of key parameters such as ambient temperature, isentropic efficiency, last-stage compression ratio, last-stage expansion ratio, reservoir temperature, hot water tank temperature, etc. is established, that is, the model includes the analytical expressions of the state variables of A-CAES, and the A-CAES nonlinear thermodynamic mathematical model is fully considered in the establishment of the day-ahead economic dispatch model of the power system, so as to obtain the power system dispatch scheme considering the variation characteristics of key parameters of A-CAES under variable working conditions.

[0031] The A-CAES structure is shown in Figure 1 , wherein the variables are marked in red font; P C , P G are compression and power generation power respectively; p atm , T atm are ambient pressure and temperature respectively; N C , N G are compression and expansion stages respectively; η C , η G are compression and expansion isentropic efficiency respectively; are the inlet working medium temperatures of the i-th compressor and the j-th expander respectively; π C.i is the compression ratio of the i-th compressor; π G.j is the expansion ratio of the j-th expander; ε, p loss are the effectiveness coefficient and pressure loss rate of the heat exchanger respectively; m st , p st , T st , T ex are the working medium mass, pressure, inlet temperature, internal temperature and wall temperature of the reservoir respectively; is the outlet heat carrier temperature of the i-th heat exchanger in the compression section; are the working medium flow rates in compression and power generation conditions respectively; T hw , T cw are the water temperatures of the hot water tank and the cold water tank respectively; m hw is the mass of the hot water tank.

[0032] The modeling assumption conditions of the A-CAES nonlinear thermodynamic mathematical model are as follows:

[0033] The model will be constructed from three aspects of compression condition, power generation condition and shutdown condition. In the modeling process, the following assumption conditions are considered:

[0034] (1) Air is used as the working medium, and it is assumed to be an ideal gas;

[0035] (2) In the compression stage, the isentropic efficiency of all compressors is consistent, and in the expansion stage, the isentropic efficiency of all expanders is consistent;

[0036] (3) The wall temperature of the gas chamber is constant;

[0037] (4) The circulating water is used as the heat carrier, which does not change phase, and all the heat exchangers have the same efficiency coefficient, and there is no heat loss in the heat exchangers and the hot water tank;

[0038] (5) In the expansion stage, the circulating water passes through the radiator to restore to normal temperature before returning to the cold water tank, so the temperature of the cold water tank is assumed to be the ambient temperature;

[0039] (6) The ambient temperature is predictable and the ambient pressure is constant within a day.

[0040] The nonlinear thermodynamic mathematical model of A-CAES in compression mode is as follows:

[0041] In compression mode, A-CAES consumes electric energy to compress the atmospheric air into high-pressure air and store it in the gas chamber. At the same time, the circulating water is pumped out from the cold water tank to absorb the heat generated during the compression process, and then flows into the hot water tank. After the compression is completed, the electric energy is converted into the pressure potential energy of the air and the heat energy of the circulating water.

[0042] (1) Compression power

[0043]

[0044] where c p is the specific heat capacity of air at constant pressure, and γ is the specific heat ratio of air.

[0045] (2) Final-stage compression ratio

[0046]

[0047] (3) Isothermal compression efficiency

[0048] The isothermal compression efficiency can be fitted as a function of the compression power, which shows a Figure 2 trend as shown in the left graph.

[0049] (4) Inlet working medium temperature of the compressor

[0050]

[0051]

[0052] (5) Inlet working medium temperature of the gas chamber

[0053]

[0054] (6) Outlet heat carrier temperature of the heat exchanger

[0055]

[0056] (7) Hot tank temperature

[0057]

[0058] where subscript "t" is the time index; Δt is the time interval between adjacent time instants; c w is the specific heat capacity of water.

[0059] (8) Hot tank mass

[0060]

[0061] (9) Air reservoir pressure

[0062]

[0063] where R is the gas constant of air; V st is the volume of the air reservoir; and a and β are the natural and forced convective heat transfer coefficients between the air in the air reservoir and the walls, respectively.

[0064] (10) Air reservoir mass

[0065]

[0066] (11) Air reservoir temperature

[0067] T st = p st V st / (m st R)

[0068] The nonlinear thermodynamic mathematical model of A-CAES in power generation mode is as follows:

[0069] In power generation mode, A-CAES releases the high-pressure air in the air reservoir, drives the turbine to rotate, and thus drives the generator to work. At the same time, the circulating water is pumped out from the hot tank, releases the compression heat absorbed previously to heat the air, improves the air doing function, and then flows through the radiator and returns to the cold tank. After the power generation is completed, the pressure potential energy of the air and the heat energy of the circulating water are converted into electric energy.

[0070] (1) Power generation power

[0071]

[0072] (2) Final stage expansion ratio

[0073]

[0074] (3) Isothermal expansion efficiency

[0075] The isothermal expansion efficiency can be fitted as a function of the power generation power, which presentsFigure 2 Trend of the right graph.

[0076] (4) Inlet working fluid temperature of expander

[0077]

[0078]

[0079] (5) Hot water tank temperature

[0080] T hw.t+1 = T hw.t

[0081] (6) Hot water tank mass

[0082]

[0083] (7) Gas holder pressure

[0084]

[0085] (8) Gas holder mass

[0086]

[0087] (9) Gas holder temperature

[0088] T st = p st V st / (m st R)

[0089] The nonlinear thermodynamic mathematical model of A-CAES under shutdown condition is as follows:

[0090] Under shutdown condition, the gas holder mass, hot water tank temperature and mass of A-CAES are constant. Since the gas in the gas holder exchanges heat with the wall of the gas holder, the pressure and temperature of the gas holder change.

[0091] (1) Hot water tank temperature

[0092] T hw.t+1 = T hw.t

[0093] (2) Hot water tank mass

[0094] m hw.t+1 = m hw.t

[0095] (3) Gas holder mass

[0096] m st.t+1 = m st.t

[0097] (4) Gas chamber pressure

[0098] p st.t+1 = p st.t - a(γ - 1)(T st.t - T ex )Δt

[0099] (5) Gas chamber temperature

[0100] T st = p st V st / (m st R)

[0101] Then, the day-ahead economic dispatch model of the power system containing the A-CAES variable condition model (i.e., the A-CAES nonlinear thermodynamic mathematical model) is constructed, and the power system is a thermal power generation system, a new energy power generation system or a hydroelectric power generation system.

[0102] Taking the power system as a thermal power generation system as an example, the day-ahead economic dispatch model of the power system containing the A-CAES is as follows:

[0103] (1) Objective function

[0104]

[0105]

[0106]

[0107]

[0108] In the formula, respectively, are the operation and start-stop costs of the thermal power unit; C WF is the wind curtailment penalty cost; T is the total number of scheduling periods; N TPU is the number of thermal power units; a TPU.k , b TPU.k are the operation cost coefficients of the kth thermal power unit; is the output of the kth thermal power unit at the t period; is the operation state of the kth thermal power unit at the t period, 0 for shutdown and 1 for startup; c TPU.k is the single start-stop cost of the kth thermal power unit; N WF is the number of wind farms; c pen is the wind curtailment penalty cost coefficient; respectively, are the predicted output and the scheduled output of the lth wind farm at the t period.

[0109] (2) A-CAES constraint

[0110] The A-CAES constraints include the state variable analytical expressions of each working condition and the operation boundary constraints. The former has been introduced in the A-CAES nonlinear thermodynamic mathematical model and will not be repeated. The latter includes the operation state constraints, power boundary constraints, reservoir pressure boundary constraints, hot water tank mass constraints, compression / generation working condition duration constraints, and the specific constraints are as follows:

[0111] u C.t +u G.t ≤1

[0112] u C.t P C.min ≤P C.t ≤u C.t P C.max

[0113] u G.t P G.min ≤P G.t ≤u G.t P G.max

[0114] p st.min ≤p st.t ≤p st.max ,t=2,3,...,T

[0115] p st.ini ≤p st.T+1 ≤p st.max

[0116] m hw.min ≤m hw.t ≤m hw.max ,t=2,3,...,T

[0117] m hw.ini ≤m hw.T+1 ≤m hw.max

[0118]

[0119]

[0120] In the formula, u C.t , u G.t are the compression and generation states of the A-CAES at the t period, and when in the compression working condition, they are respectively 1 and 0, and when in the generation working condition, they are respectively 0 and 1; P C.max , P C.min are the maximum and minimum values of the compression power; P G.max , P G.min are the maximum and minimum values of the generation power; p st.max , p st.min , p st.iniare the maximum, minimum and initial values of the gas storage pressure, respectively; m hw.max , m hw.min , m hw.ini are the maximum, minimum and initial values of the hot water tank mass, respectively; T C , T G are the maximum continuous working time of compression and power generation conditions, respectively.

[0121] (3) Other constraints

[0122] In addition to the A-CAES constraints, the day-ahead economic dispatch model also includes thermal power unit constraints (thermal power unit power constraints, wind power constraints, ramp rate constraints, start-up and shut-down time constraints), power system constraints (power flow constraints, line power constraints, power balance constraints, reserve constraints, etc.). These constraints are very mature and basic, and can be referred to relevant materials, and will not be repeated here.

[0123] It can be understood that, since the power system includes A-CAES, the power balance constraint and the reserve constraint should consider the output of both the generator unit and the A-CAES. Taking the thermal power system as an example, correspondingly, the power balance constraint is:

[0124]

[0125] In the formula, P L.t is the load forecast power.

[0126] The positive reserve constraint is:

[0127]

[0128] In the formula, P is the rated output of the thermal power unit, E L.t are the power prediction errors of the wind power and the load, respectively.

[0129] The negative reserve constraint is:

[0130]

[0131] In the formula, P is the minimum technical output of the thermal power unit.

[0132] In solving the above dispatch model, existing methods can be used for solving, for example, the most common piecewise linearization solving method, or artificial intelligence algorithms such as particle swarm algorithm.

[0133] Considering that the A-CAES has complex variable working condition electric-thermal-gas coupling characteristics, the optimization scheduling thereof is a nonlinear scheduling problem, and existing methods for solving the nonlinear scheduling problem mainly include a piecewise linearization method and a heuristic algorithm, wherein the piecewise linearization method fits nonlinear factors (such as continuous variable multiplication and nonlinear functions) of an A-CAES model in an original scheduling problem into a piecewise function, thereby reducing the difficulty of solving, but exaggerating the performance of the A-CAES in scheduling, and thus scheduling plans that do not meet the actual operation safety requirements of the A-CAES are formulated; the heuristic algorithm can be summarized as a local directivity traversal method, and this method does not have rigorous analytical optimality, and has problems such as local optimality and large time cost. Based on this, as a further preferred embodiment of the present application, a power system day-ahead economic scheduling model considering the variable working condition characteristics of the A-CAES is solved under preset constraints, including:

[0134] S1, let t = 1;

[0135] S2, the compressed power of the t period obtained by solving the power system day-ahead economic scheduling model under target constraints and the power generation power are substituted into an analytical expression of a state variable, so as to obtain the reservoir pressure and the hot water tank mass

[0136] of the t+1 period that satisfy the analytical expression.

[0137] S3, it is judged whether t is less than T, if yes, S4 is entered, otherwise, S6 is entered;

[0138] S4, it is judged whether any of or occurs, if yes, the corresponding constraint or is added to the target constraint, and then S1 is returned, otherwise, S5 is entered;

[0139] S5, let t = t + 1, and then S2 is returned until t = T;

[0140] S6, it is judged whether any of or occurs, if yes, the corresponding constraint

[0141] Return to S1 after increasing to the target constraint, otherwise end, exit iteration and output the solution result of the day-ahead economic dispatch model of the power system under the target constraint in S2.

[0142] Specifically, the embodiment of the present application preferably adopts the following method to iteratively solve the above dispatch model:

[0143] The above original dispatch problem is divided into a main problem and a sub-problem. The main problem is still a dispatch problem, but the nonlinear A-CAES constraint set (i.e. the analytical expression of the state variable of each working condition) in the constraint condition is replaced by a more idealized linear A-CAES constraint set. The sub-problem is a checking problem, which is used to check whether the dispatch instruction of the main problem to the A-CAES can meet its operation requirements. The main problem and the sub-problem are iteratively solved to realize the solution of the day-ahead economic dispatch model. That is, the solution of the main problem is to replace the analytical expression of the state variable of the A-CAES in each working condition in the preset constraint with the linear A-CAES constraint set of the state variable to solve the dispatch model; the solution of the sub-problem is to solve the value of the state variable of the A-CAES that meets the analytical expression of the state variable of the A-CAES in each working condition.

[0144] (1) Main problem

[0145] Compared with the original dispatch problem, the main problem deletes the analytical expression of the A-CAES nonlinear thermal dynamic mathematical model in the original dispatch problem, and adds the following linear constraints:

[0146]

[0147]

[0148]

[0149]

[0150]

[0151]

[0152]

[0153]

[0154]

[0155]

[0156] In the formula, respectively, are the pressure change rates of the gas storage chamber in the compression and power generation working conditions at time t; The maximum and minimum values of the pressure variation rate of the gas storage chamber under compression condition, respectively; The maximum and minimum values of the pressure variation rate of the gas storage chamber under power generation condition, respectively; The maximum and minimum values of the mass variation rate of the hot water tank under compression condition, respectively; The maximum and minimum values of the mass variation rate of the hot water tank under power generation condition, respectively; The maximum and minimum values of the mass variation rate of the hot water tank under power generation condition, respectively; M is a large positive number.

[0157] The boundary of the pressure or mass variation rate The upper and lower limit boundary functions of the pressure variation rate of the gas storage chamber and the upper and lower limit boundary functions of the mass variation rate of the hot water tank are fitted as a linear function of power, and the fitting method is as follows:

[0158] (1) Compression condition

[0159] The initial pressure of the gas storage chamber is set as the minimum value, the initial temperature is the wall temperature of the gas storage chamber, and compression is carried out under the given power and ambient temperature until the pressure of the gas storage chamber reaches the maximum value. The derivative of the gas storage chamber pressure and the mass of the hot water tank during this process can obtain the pressure variation rate of the gas storage chamber and the mass variation rate of the hot water tank during the entire compression process, and the maximum and minimum values thereof are the upper and lower limits of the pressure variation rate of the gas storage chamber and the mass variation rate of the hot water tank under the current power condition. Change the compression power and repeat the above process to obtain the function of the upper and lower limits of the pressure variation rate of the gas storage chamber and the mass variation rate of the hot water tank with the change of power.

[0160] (2) Power generation condition

[0161] The initial pressure of the gas storage chamber is set as the maximum value, and expansion power generation is carried out under the given power, gas storage chamber temperature and hot water tank temperature until the pressure of the gas storage chamber reaches the minimum value. The derivative of the gas storage chamber pressure and the mass of the hot water tank during this process can obtain the pressure variation rate of the gas storage chamber and the mass variation rate of the hot water tank during the entire power generation process, and the maximum and minimum values thereof are the upper and lower limits of the pressure variation rate of the gas storage chamber and the mass variation rate of the hot water tank under the current power condition. Change the power generation power and repeat the above process to obtain the function of the upper and lower limits of the pressure variation rate of the gas storage chamber and the mass variation rate of the hot water tank with the change of power.

[0162] In the main problem, the pressure or mass variation rate The boundary range can be freely selected, is a dispatchable decision variable, and is only affected by power. In fact, these rates belong to state variables and are affected by multiple factors including power. For example, the rate of change of the reservoir pressure under compression is a function of the compression power, reservoir pressure, and ambient temperature. The main problem's treatment of pressure or mass rate makes the performance of A-CAES overestimated in the scheduling process. In addition, the main problem ignores the heat exchange between the air in the reservoir and the wall. This also reflects the overoptimistic performance of A-CAES. Therefore, after the main problem is solved, the scheduling result needs to be further checked by the sub-problem to see if it exceeds the operating limits of A-CAES.

[0163] The more idealized A-CAES model in the main problem is necessary, which leaves room for iterative convergence. If the A-CAES model in the main problem is more conservative, the scheduling result of the main problem will certainly meet the operating requirements of A-CAES in the sub-problem checking. However, this scheduling result is not a globally optimal solution because the large-scale energy throughput of A-CAES is not fully utilized. Therefore, a more conservative A-CAES model is not desirable in the main problem.

[0164] (2) Sub-problem

[0165] Since the idealized A-CAES model of the main problem performs better than the original scheduling problem, the A-CAES charge and discharge power plan obtained by the main problem may be too optimistic and aggressive, leading to the reservoir pressure or hot water tank mass of A-CAES exceeding the safety boundary during actual operation. Therefore, further checking by the sub-problem is needed before the power command is issued to ensure the safety of A-CAES operation. The checking steps of the sub-problem are as follows:

[0166] ① t is 1.

[0167] ② The main problem passes to the sub-problem. and are the compression power and power generation of the t period obtained by the main problem. and are the reservoir pressure and hot water tank mass of the t+1 period obtained by the main problem.

[0168] ③ The sub-problem substitutes and into the A-CAES nonlinear thermal dynamic mathematical model of step A, and calculates and and are the reservoir pressure and hot water tank mass of the t+1 period obtained by the sub-problem.

[0169] If t < T, execute this step, otherwise skip.

[0170] If A-CAES overcharging, overcharging amount is To avoid the reservoir pressure at time t+1 exceeding the upper bound, in the next iteration, the main problem needs to add the following constraints:

[0171]

[0172] If the case occurs, it can also be handled in the above method. In the next iteration, the main problem needs to add the following constraints:

[0173]

[0174]

[0175]

[0176] If t = T, execute this step, otherwise skip.

[0177] In order to facilitate the use of the next scheduling day, at time T+1, the lower bound of the reservoir pressure should be p st.ini instead of p st.min , and the lower bound of the hot water tank mass is m hw.ini instead of m hw.min . Therefore, if the case occurs, in the next iteration, the main problem needs to add the following constraints:

[0178]

[0179]

[0180]

[0181]

[0182] This step needs to choose one of the following three cases to execute:

[0183] [Case 1] If in ④ or ⑤ the sub-problem finds that the reservoir pressure or the hot water tank mass at time t+1 exceeds the boundary, stop checking, after adding the relevant constraints, start the next iteration.

[0184] [Case 2] If the reservoir pressure and the hot water tank mass at time t+1 do not exceed the boundary and t < T, then t = t+1, return to step ② to continue checking.

[0185] [Case 3] If the gas storage chamber pressure and the hot water tank mass at time T+1 do not exceed the boundaries, it indicates that the gas storage chamber pressure and the hot water tank mass in all time periods meet the upper and lower limit constraints. At this point, the algorithm has converged, the iteration is exited, and the solution result of the main problem is output.

[0186] The model solution result is as follows:

[0187] 1) thermal power unit: start-stop state, planned output

[0188] 2) wind farm: planned output

[0189] 3) A-CAES: compression state, power generation state, compression power, power generation power, gas storage chamber pressure, and hot water tank mass in each scheduling time period

[0190] 4) other: total system cost

[0191] The method provided by the embodiment of the application is further described below with a specific example.

[0192] The embodiment is based on the day-ahead economic dispatch strategy of the power system considering the variable working condition characteristics of large-scale A-CAES described in the application, and the specific steps are as follows:

[0193] Step A: The model of the embodiment is as follows.

[0194] (1) The scheduling model is constructed with the minimum sum of thermal power unit operation cost, thermal power unit start-stop cost, and wind power penalty cost as the optimization objective, considering the thermal power unit operation constraint, A-CAES operation constraint, wind farm operation constraint, and system operation constraint.

[0195] (2) In the above model, the specific mathematical expressions are described in the A-CAES nonlinear thermal dynamic mathematical model and the day-ahead economic dispatch model of the power system containing A-CAES in steps A and B of the summary.

[0196] Step B: The parameter settings of the embodiment are as follows.

[0197] (1) The embodiment is tested on a computer with a CPU model of Intel Xeon Gold 2.70GHz and a memory of 256GB, the scheduling model is solved by MATLAB R2022a calling Yalmip, and the solver is Gurobi9.1.

[0198] (2) The optimization time scale of the embodiment: the total scheduling time is 1d, and the unit scheduling time is 15min.

[0199] (3) The topological graph of the embodiment: the topological graph is as shown in Figure 3

[0200] ​(4) Example A-CAES parameters: as shown in Table 1.

[0201] Table 1

[0202]

[0203] (5) Example thermal power unit parameters: as shown in Table 2.

[0204] Table 2

[0205]

[0206] (6) Example wind power, load power, and ambient temperature prediction curves: as shown in Figure 4 .

[0207] Step C: Optimize and analyze the results of the example.

[0208] To verify the effectiveness of the day-ahead economic dispatch strategy for power systems considering the variable operating condition characteristics of A-CAES and the fast solving method proposed in the present application, four groups of examples, Case 1, Case 2, Case 3, and Case 4, were set up. Case 1 contains one A-CAES and is solved by the iterative method proposed in the present application. Case 2 contains one A-CAES and is solved by the traditional piecewise linearization method. Case 3 contains two A-CAESs and is solved by the iterative method proposed in the present application. Case 4 contains three A-CAESs and is solved by the iterative method proposed in the present application.

[0209] First, the results of Case 1 and Case 2 were compared to analyze the advantages of the iterative solving method. The A-CAES power of the two cases is shown in Figure 5 , the gas storage chamber pressure is shown in Figure 6 , the hot water tank mass is shown in Figure 7 , and the total cost is shown in Table 3.

[0210] Table 3

[0211]

[0212] As shown in Table 3, the solving time of Case 2 is as long as 40,000 s, far exceeding the 412 s of Case 1, and completely not meeting the timeliness requirements of day-ahead economic dispatch. The slow solving of Case 2 is fundamentally due to the introduction of a large number of binary auxiliary variables when applying the piecewise linearization method to the thermal dynamic mathematical model of A-CAES, resulting in an excessively large size of the dispatch model and a sharp increase in solving difficulty. In terms of total cost, Case 2 seems to have a slight advantage. However, in fact, this is a false result obtained by overestimating the performance of A-CAES.

[0213] As shown in Figure 5It can be seen that, thanks to the strong optimization ability of the iterative solution method, the energy throughput and operation time of A-CAES in Case 1 far exceed those in Case 2, which makes A-CAES more thoroughly and fully play the value of energy type energy storage.

[0214] By Figure 6 It can be seen that the pressure of the gas storage chamber in Case 1 is always within the specified boundary. In contrast, the piecewise linearization method overestimates the performance of A-CAES, which is specifically manifested in that, at the same compression power, the pressure increase of "DispatchResults" is greater than that of "ActualResults"; at the same power generation, the pressure consumption of "DispatchResults" is less than that of "ActualResults". After one day of error accumulation, when "DispatchResults" considers that the pressure at the first dispatch time of the next day meets the boundary constraint, "ActualResults" has been far below the initial pressure. This shows that using the piecewise linearization method to solve the dispatch model will obtain an overly optimistic A-CAES power plan, which cannot be realized in the actual operation of A-CAES.

[0215] Figure 7 The same law as Figure 6 Since the hot water tank mass does not exceed the limit in Case 1 and Case 2, it will not be described again.

[0216] Secondly, by comparing the results of Case 1, Case 3 and Case 4, the influence law of the scale of the dispatch problem on the solving efficiency of the method is explored. The results of the three cases are shown in Table 4.

[0217] Table 4

[0218]

[0219] As can be seen from Table 4, as the number of A-CAES in the dispatch model increases, the solving time is always maintained within 10 minutes. This is because, in the method, the nonlinear model of A-CAES appears in the subproblem checking process. This process is simply a formula calculation and does not involve optimization solving. It can be predicted that even if more A-CAES are connected to the system, the solving time will not increase too much. However, if the existing piecewise linearization method is used, the number of binary auxiliary variables will increase sharply with the increase of the number of A-CAES, resulting in slower solving of the dispatch model, or even unable to solve.

[0220] An electronic device is provided, including a computer readable storage medium and a processor;

[0221] The computer readable storage medium is used to store executable instructions;

[0222] The processor is configured to read the executable instructions stored in the computer readable storage medium and execute the method according to any one of the above embodiments.

[0223] The embodiment of the present application provides a computer readable storage medium, which stores computer instructions, and the computer instructions are used for causing a processor to execute the method according to any one of the above embodiments.

[0224] The embodiment of the present application provides a computer program product, which comprises computer programs or instructions, and the computer programs or instructions are executed by a processor to realize the method according to any one of the above embodiments. It should be understood by those skilled in the art that the above description is merely preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A power system dispatching method considering the nonlinear variable operating condition characteristics of large-scale advanced compressed air energy storage, characterized in that: include: With the goal of minimizing the total operating cost of the power system, a day-ahead economic dispatch model for the power system is established that takes into account the variable operating characteristics of the A-CAES. The model is solved under preset constraints to obtain the optimal dispatch plan for the generators and A-CAES in the power system. The preset constraints include: generator set constraints, power system constraints and A-CAES constraints; the A-CAES constraints include the operating boundary constraints of the A-CAES state variables under various operating conditions and the analytical expressions of the state variables; The power system is a thermal power generation system, a new energy power generation system or a hydropower generation system; Solving the power system day-ahead economic dispatch model under preset constraints includes: S1, order t =1; S2, which is obtained by solving the power system day-ahead economic dispatch model under the target constraint, t Compression power of the time period and power generation Substitute the analytical expression of the state variable to obtain t +1 period of air storage chamber pressure and hot water tank quality ; The target constraint is obtained by replacing the analytical expression of the A-CAES state variables under each working condition in the preset constraints with the linear A-CAES constraint set of the state variables; S3, judgment t Is it less than T If yes, go to S4, otherwise go to S6; T is the total number of scheduling periods; S4, determine whether 、 、 or In any case, if so, the corresponding constraint 、 、 or After adding to the target constraint, return to S1, otherwise enter S5; S5, order t = t +1, return to S2, until t = T ; S6, determine whether 、 、 or In any case, if so, the corresponding constraint 、 、 、 After adding to the target constraint, return to S1, otherwise end; in, and are obtained by solving the power system day-ahead economic dispatch model under target constraints. t +1 period of gas chamber pressure and hot water tank mass, 、 、 are the maximum, minimum and initial values ​​of the air storage chamber pressure, respectively. 、 They are respectively obtained by solving the power system day-ahead economic dispatch model under the target constraint. T Compression power of the time period and power generation Substitute the analytical expression of the state variable to obtain the T +1 period of gas chamber pressure and hot water tank mass; The linear A-CAES constraint set of the state variables is: in, 、 Under compression and power generation conditions respectively t The rate of change of the air storage chamber pressure during the period; 、 They are the maximum and minimum values ​​of the pressure change rate of the air storage chamber under compression conditions; 、 They are the maximum and minimum values ​​of the pressure change rate of the gas storage chamber under power generation conditions; 、 Under compression and power generation conditions respectively t The rate of change of hot water tank mass during the period; 、 They are the maximum and minimum values ​​of the mass change rate of the hot water tank under compression conditions; 、 They are the maximum and minimum values ​​of the mass change rate of the hot water tank under power generation conditions; 、 They are t, t +1 period of air storage chamber pressure, for t +1 period of hot water tank quality; M is a positive number; 、 A-CAES t Compression of time periods and power generation status.

2. The method according to claim 1, wherein 、 、 、 、 、 、 、 They are all fitted as a linear function of power, and the fitting process is: Under compression conditions, the initial pressure of the air storage chamber is set to the minimum value, and the initial temperature is the air storage chamber wall temperature. Compression is performed at a given power and ambient temperature until the air storage chamber pressure reaches its maximum value. The derivatives of the air storage chamber pressure and the hot water tank mass during this process are taken to obtain the rate of change of the air storage chamber pressure and the rate of change of the hot water tank mass throughout the compression process. The maximum and minimum values ​​are calculated to be the upper and lower limits of the rate of change of the air storage chamber pressure and the rate of change of the hot water tank mass under the current power conditions. The compression power is varied and the above process is repeated to obtain the upper and lower limits of the rate of change of the air storage chamber pressure and the rate of change of the hot water tank mass as a function of power. Under power generation conditions, the initial pressure of the gas storage chamber is set to the maximum value, and expansion power generation is carried out at a given power, gas storage chamber temperature, and hot water tank temperature until the gas storage chamber pressure reaches the minimum value; the derivative of the gas storage chamber pressure and the hot water tank mass in this process is taken to obtain the gas storage chamber pressure change rate and the hot water tank mass change rate of the entire power generation process. The maximum and minimum values ​​are calculated, which are the upper and lower limits of the gas storage chamber pressure change rate and the hot water tank mass change rate under the current power conditions; the power generation power is changed, and the above process is repeated to obtain the upper and lower limits of the gas storage chamber pressure change rate and the hot water tank mass change rate as a function of power change.

3. The method according to claim 1, wherein Under compression conditions, the state variables of the A-CAES include compression power, final stage compression ratio, isentropic compression efficiency, compressor inlet working medium temperature, air storage chamber inlet working medium temperature, heat exchanger outlet heat carrier temperature, hot water tank temperature and mass, and air storage chamber pressure, mass, and temperature. Under power generation conditions, the state variables of the A-CAES include power generation power, final stage compression ratio, isentropic expansion efficiency, inlet working medium temperature of the expander, temperature and mass of the hot water tank, and pressure, mass, and temperature of the gas storage chamber. Under shutdown conditions, the state variables of the A-CAES include the temperature and mass of the hot water tank, and the pressure, mass, and temperature of the gas storage chamber.

4. An electronic device, characterized in that: include: Computer-readable storage medium and processor; The computer-readable storage medium is used to store executable instructions; The processor is configured to read the executable instructions stored in the computer-readable storage medium and execute the method according to any one of claims 1 to 3.

5. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to execute the method according to any one of claims 1 to 3.

6. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

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