Collaborative optimization scheduling method for power system of abandoned underground coal mine and related equipment

Abstract

The embodiment of the invention provides an electric power system collaborative optimization scheduling method for an abandoned underground coal mine and related equipment, and relates to the technical field of energy scheduling, and the method comprises the steps: building a battery-like model suitable for electric power system optimization scheduling based on the gas storage gas pressure of the abandoned underground coal mine and an AA-CAES mathematical model; respectively building a day-ahead scheduling model taking economy as a target, an intra-day scheduling model taking low-carbon property as a target and a real-time scheduling model taking stability as a target based on the battery-like model; and based on the day-ahead scheduling model, the intra-day scheduling model and the real-time scheduling model, performing power system collaborative optimization scheduling. According to the technical scheme of the invention, the multi-time scale characteristics of AA-CAES, wind, light and load are considered, and the time scale characteristics of various requirements under the background of a novel power system are also considered.

Description

Technical Field

[0001] The present application relates to the field of energy dispatching technology, and more specifically, to a method for coordinated optimization dispatching of power systems in abandoned underground coal mines and related equipment. Background Art

[0002] Energy storage is the key support for building a new power system in the context of mismatch between energy supply and demand. Due to the optimization and adjustment of the energy structure, there are a large number of abandoned underground coal mines with huge available space in my country, which can be combined with the new advanced adiabatic compressed air energy storage (AA-CAES) to achieve the reuse of abandoned underground space while improving the support capacity of the power grid. However, abandoned underground space energy storage is different from conventional new energy storage in scheduling planning, and the cost of intrinsic safety transformation and reinforcement needs to be considered. In addition, AACAES is much more complex in structure than other physical energy storage technologies, coupling mechanical energy, potential energy, thermal energy and electrical energy. It is not practical to consider all physical variables and the complete AA-CAES model in one operation problem. Therefore, it is necessary to adopt a reasonably simplified AA-CAES mathematical model suitable for optimal scheduling. In addition, the addition of AA-CAES adds new problems to the optimal scheduling of the power system. Therefore, it is of great significance to study the coordinated optimal scheduling of AA-CAES access to the power grid and new energy.

[0003] At present, the optimization dispatch model of AA-CAES mostly adopts a linear model with fixed efficiency, without considering the flexible characteristics of AA-CAES at multiple time scales, such as start-stop, ramp-up, and efficiency. Inaccurate dispatch models may cause large deviations in the calculation of AA-CAES efficiency, charging and discharging power, and actual energy storage level of AA-CAES, and affect the economy and feasibility of the optimization dispatch results. At present, there is a problem of relatively single dispatch objectives for the optimization dispatch containing AA-CAES, which cannot meet the multi-faceted requirements of the new power system. In addition, in terms of time scale, most of the research is on the day-ahead scale, lacking consideration of the multi-time scale characteristics of AA-CAES, wind and solar, and load, and multi-time scale optimization dispatch. Summary of the invention

[0004] The embodiments of the present application provide a method and related equipment for collaborative optimization and scheduling of the power system of an abandoned underground coal mine to solve the technical problems existing in the prior art.

[0005] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0006] According to a first aspect of an embodiment of the present application, a method for collaborative optimization and scheduling of a power system of an abandoned underground coal mine is provided, comprising:

[0007] A battery-like model suitable for optimal dispatching of power systems is established based on the gas storage pressure of abandoned underground coal mines and the AA-CAES mathematical model;

[0008] Based on the battery-like model, a day-ahead dispatch model with economy as the goal, an intraday dispatch model with low carbon as the goal, and a real-time dispatch model with stability as the goal are respectively constructed;

[0009] The power system is coordinated and optimized based on the day-ahead dispatch model, the intraday dispatch model and the real-time dispatch model.

[0010] In some embodiments of the present application, based on the above-mentioned solution, the gas storage pressure of the abandoned underground coal mine and the AA-CAES mathematical model are used to establish a battery-like model suitable for optimizing the dispatch of the power system, including:

[0011] Establish a simplified AA-CAES mathematical model;

[0012] Normalize the gas storage pressure;

[0013] The battery model is derived based on the simplified AA-CAES mathematical model and the normalized processing results.

[0014] In some embodiments of the present application, based on the above solution, a day-ahead scheduling model with economy as the goal is built based on the battery-like model, including:

[0015] The day-ahead dispatch objective function is established with the economic efficiency of the power system as the goal. The day-ahead dispatch objective function includes: AA-CAES daily equivalent investment cost, thermal power unit operation and maintenance cost and start-up and shutdown cost, AA-CAES unit operation and maintenance cost and start-up and shutdown cost, wind and solar operation and maintenance cost and wind and solar abandonment cost, network loss cost and electricity sales and purchase cost;

[0016] Determine the day-ahead scheduling decision variables;

[0017] The day-ahead dispatch constraints are established based on the battery-like model, wherein the day-ahead dispatch constraints include: AA-CAES related constraints, thermal power unit operation constraints, wind and solar power output constraints, power balance constraints and transmission line capacity constraints.

[0018] In some embodiments of the present application, based on the above scheme, a daytime scheduling model with low carbon as the goal is built based on the battery-like model, including:

[0019] An intraday dispatch objective function is established with the low carbon nature of the power system as the goal, and the intraday dispatch objective function includes the amount of wind and solar power abandoned and carbon emissions;

[0020] Determine intraday scheduling decision variables;

[0021] Based on the battery-like model, intraday scheduling constraints are established, wherein the intraday scheduling constraints include: AA-CAES related constraints, thermal power unit operation constraints, wind and solar output constraints, power balance constraints and transmission line capacity constraints.

[0022] In some embodiments of the present application, based on the above solution, a real-time scheduling model with stability as the goal is built based on the battery-like model, including:

[0023] A real-time dispatch objective function is established with the stability of the power system as the goal. The real-time dispatch objective function includes the net load variance of the power grid, the wind and solar power output deviation, and the AA-CAES output deviation;

[0024] Determine real-time scheduling decision variables;

[0025] Real-time scheduling constraints are established based on the battery-like model, wherein the real-time scheduling constraints include: AA-CAES related constraints, thermal power unit standby constraints, wind and solar power output constraints, power balance constraints and transmission line capacity constraints.

[0026] In some embodiments of the present application, based on the aforementioned solution, the coordinated optimization scheduling of the power system based on the day-ahead scheduling model, the intraday scheduling model and the real-time scheduling model includes:

[0027] Obtain the forecast data of wind, solar and load output in the day-ahead manner and input them into the day-ahead dispatch model for processing to obtain the output plan of wind, solar, thermal and AA-CAES and the start and stop plan of thermal power units in the next 24 hours;

[0028] Obtaining the intraday wind, light, and load output forecast data, and inputting the intraday wind, light, and load output forecast data, the output plans of wind, light, fire, and AA-CAES in the next 24 hours, and the start and stop plans of thermal power units into the intraday scheduling model for processing, and obtaining the wind and light output plans in the next hour, the start and stop plans of AA-CAES units, the output plans, and the intraday correction plans of thermal power unit output;

[0029] Real-time wind, solar and load output forecast data are obtained, and the real-time wind, solar and load output forecast data, the wind and solar output plan for the next hour, the AA-CAES unit start and stop, the output plan and the thermal power unit output intraday correction plan are input into the real-time scheduling model for processing to obtain the wind and solar output plan for the next 15 minutes, the thermal power unit output real-time correction plan and the energy storage unit output real-time correction plan.

[0030] According to a second aspect of an embodiment of the present application, a power system collaborative optimization scheduling device for an abandoned underground coal mine is provided, comprising:

[0031] The first establishment unit is used to establish a battery-like model suitable for optimal dispatching of the power system based on the gas storage pressure of the abandoned underground coal mine and the AA-CAES mathematical model;

[0032] A second establishment unit is used to respectively establish a day-ahead dispatch model with economy as the goal, an intraday dispatch model with low carbon as the goal, and a real-time dispatch model with stability as the goal based on the battery-like model;

[0033] A dispatching unit is used to perform coordinated optimization dispatching of the power system based on the day-ahead dispatching model, the intraday dispatching model and the real-time dispatching model.

[0034] According to a third aspect of an embodiment of the present application, a computer-readable storage medium is provided, wherein the storage medium stores computer instructions, and when the computer instructions are executed on a computer, the computer executes the method according to the first aspect.

[0035] According to a fourth aspect of the embodiments of the present application, there is provided an electronic device, including: a memory and a processor;

[0036] The memory is used to store computer instructions;

[0037] The processor is used to call the computer instructions stored in the memory so that the electronic device executes the method as described in the first aspect.

[0038] The technical solution of the present application realizes the optimal dispatching of the power system through a day-ahead dispatching model with economy as the goal, an intraday dispatching model with low carbon as the goal, and a real-time dispatching model with stability as the goal. The technical solution of the present application takes into account the multi-time scale characteristics of AA-CAES, wind, solar and load, as well as the time scale characteristics of various requirements in the context of the new power system.

[0039] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0041] Figure 1 A schematic diagram of a process of a method for coordinated optimization and scheduling of a power system of an abandoned underground coal mine according to an embodiment of the present application is shown;

[0042] Figure 2 A schematic diagram of a process for performing coordinated optimization scheduling of a power system based on a day-ahead scheduling model, an intraday scheduling model, and a real-time scheduling model according to an embodiment of the present application is shown;

[0043] Figure 3 A modified IEEE33 node topology diagram according to an embodiment of the present application is shown;

[0044] Figure 4 A schematic diagram of day-ahead dispatching of wind and solar power output considering economic efficiency according to an embodiment of the present application is shown;

[0045] Figure 5 A schematic diagram of wind and solar power output considering multi-time scale optimization scheduling according to an embodiment of the present application is shown;

[0046] Figure 6 A schematic diagram showing the output results of each unit scheduled on the day before considering economy according to an embodiment of the present application is shown;

[0047] Figure 7 A schematic diagram of the output results of each unit under multi-time scale optimization scheduling according to an embodiment of the present application is shown;

[0048] Figure 8 A block diagram of a power system collaborative optimization dispatching device for an abandoned underground coal mine according to an embodiment of the present application is shown;

[0049] Fig. 9 A block diagram of an electronic device according to an embodiment of the present application is shown;

[0050] Fig.10 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0051] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0052] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0053] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0054] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0055] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the objects used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those shown or described.

[0056] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] Some embodiments of the present application will be described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.

[0058] See also Figure 1 , showing a flow chart of a method for collaborative optimization and scheduling of a power system of an abandoned underground coal mine according to an embodiment of the present application.

[0059] like Figure 1 As shown, a method for collaborative optimization and scheduling of power systems in abandoned underground coal mines is presented, including steps S100 to S300.

[0060] refer to Figure 1 , step S100, establishing a battery-like model suitable for optimal scheduling of power systems based on the gas storage pressure of the abandoned underground coal mine and the AA-CAES mathematical model.

[0061] In some feasible embodiments, based on the above scheme, the gas storage pressure of the abandoned underground coal mine and the AA-CAES mathematical model are used to establish a battery-like model suitable for optimizing the dispatch of the power system, including:

[0062] Establish a simplified AA-CAES mathematical model;

[0063] Normalize the gas storage pressure;

[0064] The battery model is derived based on the simplified AA-CAES mathematical model and the normalized processing results.

[0065] Exemplarily, the specific process of step S100 is as follows:

[0066] Step S110, establishing a reasonably simplified AA-CAES mathematical model.

[0067] First, some assumptions are made: (1) the heat capacity ratio of air and oil is fixed, (2) the inlet oil temperature of the high-temperature heat storage is constant, and (3) the temperature in the gas storage is fixed to the ambient temperature.

[0068] The simplified AA-CAES model is given below:

[0069]

[0070] In the formula is the pressure in the gas storage during period t, and its upper and lower limits are k is the specific heat ratio of air, R is the universal gas constant, V AS is the volume of the gas storage reservoir, is the air mass flow rate flowing into the gas storage during period t, is the air mass flow rate outflowing during period t, T AS is the air temperature in the gas storage, and Δt is the unit scheduling time.

[0071]

[0072] φ cmin and φ cmax are the minimum and maximum charging power functions, respectively, dmin and φ dmax are the minimum and maximum power generation functions respectively. According to the above formula, we can see that the charging power With discharge power The feasible range depends on

[0073]

[0074] γ c , γ d is the external characteristic function of the system under optimal efficiency. According to the above formula, we can see that the mass flow rate is related to gas pressure and power.

[0075] Step S120: deriving a battery-like model of AA-CAES.

[0076] Normalize the gas storage pressure:

[0077]

[0078] E t is the normalized gas pressure of the gas storage, and its value range is [0,1]. The battery-like model of AA-CAES can be obtained through the above formula:

[0079]

[0080] in,

[0081]

[0082] where k c and k d They represent the reciprocals of the rated charging time and the rated discharging time, respectively. The symbol . represents the corresponding superscripts cmin, cmax, dmin, and dmax. The efficiency characteristic function Γ and the capacity characteristic function Φ are related to the operation mode and specific control method of the AA-CAES system. The characteristic functions of AA-CAES under different operation modes are shown in Table 1.

[0083] Table 1 Characteristic functions of AA-CAES under different operation modes

[0084]

[0085]

[0086] refer to Figure 1, step S200, based on the battery-like model, a day-ahead scheduling model with economy as the goal, an intraday scheduling model with low carbon as the goal, and a real-time scheduling model with stability as the goal are respectively established.

[0087] It is understandable that step S200 can be specifically divided into:

[0088] Step S210, building a day-ahead scheduling model with economy as the goal based on the battery-like model;

[0089] Step S220, building a daily scheduling model with low carbon as the goal based on the battery-like model;

[0090] Step S230, building a real-time scheduling model with stability as the goal based on the battery-like model.

[0091] It should be noted that the day-ahead dispatch model is executed once every 24 hours with a resolution of 1 hour. It uses 24-hour forecast data of wind, light, and load to optimize the output plans of wind, light, thermal power, and AA-CAES for the next 24 hours, and uses the start and stop plans of thermal power units as known quantities for intraday and real-time dispatching.

[0092] Intraday dispatch model: It is executed once every hour with a resolution of 15 minutes. It uses the wind, light and load forecast data one hour in advance and the thermal power start and stop plan scheduled on the day before to optimize the wind and light output, AA-CAES start and stop, output plan and thermal power output correction plan in the next hour, and substitute them as known quantities for real-time dispatch.

[0093] Real-time dispatch model: It is executed every 15 minutes with a resolution of 5 minutes. It uses the 15-minute advance forecast data of wind, solar and load, the thermal power start and stop plan for day-ahead dispatch and the AA-CAES start and stop plan for intraday dispatch to optimize the real-time correction plan of wind, solar output, thermal power output and AA-CAES output in the next 15 minutes.

[0094] In some feasible embodiments, based on the above solution, step S210 includes:

[0095] The day-ahead dispatch objective function is established with the economic efficiency of the power system as the goal. The day-ahead dispatch objective function includes: AA-CAES daily equivalent investment cost, thermal power unit operation and maintenance cost and start-up and shutdown cost, AA-CAES unit operation and maintenance cost and start-up and shutdown cost, wind and solar operation and maintenance cost and wind and solar abandonment cost, network loss cost and electricity sales and purchase cost;

[0096] Determine the day-ahead scheduling decision variables;

[0097] The day-ahead dispatch constraints are established based on the battery-like model, wherein the day-ahead dispatch constraints include: AA-CAES related constraints, thermal power unit operation constraints, wind and solar power output constraints, power balance constraints and transmission line capacity constraints.

[0098] Exemplarily, based on the above example, the specific process of step S210 is as follows:

[0099] Step S211, constructing a day-ahead scheduling objective function.

[0100] Day-ahead dispatch takes the economic efficiency of the system as the goal F1, which mainly includes the AA-CAES daily equivalent investment cost C inv , thermal power unit operation and maintenance cost C tho and start-stop cost C ths , AA-CAES unit operation and maintenance cost C CAESo and start-stop cost C CAESs , Wind and solar operation and maintenance costs C wvo and the cost of wind and solar power abandonment C wvw , network loss cost C loss And the cost of electricity sales and purchase C trans .

[0101] F1=min(C inv +C tho +C ths +C AA-CAESo +C AA-CAESs +C wvo +C wvw +

[0102] C loss +C trans );

[0103]

[0104] Among them, c inv,AA-CAES is the unit capacity investment cost of AA-CAES considering the transformation cost of abandoned underground coal mines, P i,AA-CAES The AA-CAES installation capacity of the i-th node, N is the number of AA-CAES installed, n is the economic useful life of flexible resources, r is the discount rate. T is the total time period in the dispatch cycle, G is the total number of thermal power units, a g , b g 、c g are the coal consumption cost coefficient of thermal power unit g, P g,t is the active power of thermal power unit g in period t in the day-ahead dispatch model, U g,t is a binary variable that represents the operating status of thermal power unit g during period t, 0 means shutdown and 1 means operation; C ths,g is the start-up and shutdown cost of thermal power unit g.AA-CAESo is the operation and maintenance cost per unit capacity of AA-CAES, z i,t c and z i,t d are 0-1 variables, representing the operating status of the compression side and the power generation side of AA-CAES unit i at time t, respectively. AA-CAESo,on 、c AA-CAESo,off are the startup cost coefficient and shutdown cost coefficient of the AA-CAES unit respectively. wo is the operation and maintenance cost per unit capacity of the wind farm, c vo is the operation and maintenance cost per unit capacity of the photovoltaic power station, c wo is the wind curtailment penalty cost coefficient, c vo is the penalty cost coefficient for abandoned light, P wpg,t , P pvg,t are the wind power and photovoltaic power generation in period t, ΔP wpg,t , ΔP pvg,t are the wind and solar curtailment amounts during period t, respectively. loss is the network loss price, P loss,t is the network loss in period t, c trans is the price of electricity purchased from the main grid, P trans,t is the electricity purchased by the main network during period t.

[0105] Step S212, clarifying the day-ahead scheduling decision variables.

[0106] It is executed once every 24 hours, once a day, with a resolution of 1 hour. The wind, light, and load forecast data for 24 hours in advance are used to optimize the output plan of wind, light, fire, and AA-CAES for the next 24 hours. The start and stop plan of the thermal power unit is used as a known quantity and substituted into the intraday scheduling and real-time scheduling model.

[0107] Step S213: Establishing day-ahead scheduling constraints.

[0108] (1) AA-CAES related constraints include capacity, start-stop, ramping, and reserve capacity constraints as follows:

[0109] The capacity constraint is:

[0110]

[0111] The start-stop constraints are:

[0112] z i,t c +z i,t d ≤1;

[0113] The climbing constraint is:

[0114]

[0115] In the formula, δ c,up , δ c,down They represent the up and down ramp rates of the compression process of AA-CAES unit i, δ d,up , δ d,down They represent the upward and downward climbing rates of the power generation process of AA-CAES unit i respectively.

[0116] The spare capacity constraint is:

[0117]

[0118] In the formula, They represent the upper and lower reserve capacity ratios of AA-CAES unit i during compression process period t, They respectively represent the upper and lower reserve capacity ratios of AA-CAES unit i during period t of the power generation process.

[0119] (2) The operating constraints of thermal power units include output constraints, ramp constraints, start-stop constraints, and standby constraints.

[0120] The output constraint is:

[0121]

[0122] Where P g,max , P g,min They respectively represent the upper and lower limits of the thermal power unit g output.

[0123] The climbing constraint is:

[0124]

[0125] In the formula, is the ramp-up rate of thermal power unit g; is the ramp-down rate of thermal power unit g.

[0126] The start-stop constraints are:

[0127]

[0128] In the formula, are the time that thermal power unit g has been continuously started and shut down from t-1 to g; They are the minimum continuous start-up and shutdown time of thermal power unit g respectively.

[0129] The alternative constraints are:

[0130]

[0131] In the formula They are the upward reserve capacity and downward reserve capacity of thermal power unit g in period t respectively.

[0132] (3) Wind and solar power output constraints

[0133]

[0134] Where P w,N is the installed capacity of the wind farm; P w,t The wind power dispatch output for period t; P is the upper and lower limits of wind farm output prediction during period t; v,N is the installed capacity of the photovoltaic power station; P v,t The photovoltaic dispatch output for period t; It is the upper and lower limits of the predicted output of the photovoltaic power station in period t.

[0135] (4) Power balance constraints

[0136]

[0137] Where P L,t is the load during period t.

[0138] (5) Transmission line capacity constraints

[0139]

[0140] In the formula, m is the node number, M is the total number of nodes, G l,m is the transfer distribution factor from node m to line l, P m,t is the active injection power of node m during period t, L l,max is the maximum transmission power of line l.

[0141] In some feasible embodiments, based on the above solution, step S220 includes:

[0142] An intraday dispatch objective function is established with the low carbon nature of the power system as the goal, and the intraday dispatch objective function includes the amount of wind and solar power abandoned and carbon emissions;

[0143] Determine intraday scheduling decision variables;

[0144] Based on the battery-like model, intraday scheduling constraints are established, wherein the intraday scheduling constraints include: AA-CAES related constraints, thermal power unit operation constraints, wind and solar output constraints, power balance constraints and transmission line capacity constraints.

[0145] Exemplarily, based on the above example, the specific process of step S220 is as follows:

[0146] Step S221, constructing the intraday scheduling objective function.

[0147] The intraday dispatch takes the low carbon nature of the system as the target F2, which mainly includes the amount of wind and solar power abandoned ΔP WV and carbon emissions C G .

[0148] F2=min(ΔP WV +C G );

[0149] ΔP WV =ΔP wpg +ΔP pvg ;

[0150]

[0151] Where E is the carbon emission coefficient.

[0152] Step S222, clarify the intraday scheduling decision variables.

[0153] It is executed once every hour, 24 times a day, with a resolution of 15 minutes. Using the wind, light, and load forecast data one hour in advance and the thermal power start and stop plan on the day before, and taking the thermal power output plan on the day before as a reference, the wind, light output, AA-CAES unit start and stop and output and thermal power correction plan for the next hour are optimized as known quantities for the real-time scheduling model.

[0154] Step S223, establishing intraday scheduling constraints.

[0155] (1) AA-CAES related constraints are on the same day.

[0156] (2) The output constraint and reserve constraint of the thermal power unit operation constraints are the same as the previous day. Since the start and stop plan of the thermal power unit was determined a few days ago, its start and stop constraints are not considered. The ramp constraint needs to be adjusted due to the change of time scale as follows:

[0157]

[0158] (3) Wind and solar power output constraints are on the same day.

[0159] (4) Power balance constraints on the same day.

[0160] (5) Transmission line capacity constraints on the same day.

[0161] In some feasible embodiments, based on the above solution, step S230 includes:

[0162] A real-time dispatch objective function is established with the stability of the power system as the goal. The real-time dispatch objective function includes the net load variance of the power grid, the wind and solar power output deviation, and the AA-CAES output deviation;

[0163] Determine real-time scheduling decision variables;

[0164] Real-time scheduling constraints are established based on the battery-like model, wherein the real-time scheduling constraints include: AA-CAES related constraints, thermal power unit standby constraints, wind and solar power output constraints, power balance constraints and transmission line capacity constraints.

[0165] Exemplarily, based on the above example, the specific process of step S230 is as follows:

[0166] Step S231, building a real-time scheduling objective function.

[0167] Real-time dispatch takes system stability as the goal F3, which mainly includes the net load variance ΔP of the power grid real,l , wind and solar power output deviation ΔP real,wv And AA-CAES output deviation ΔP real,AA-CAES .

[0168] F3=min(ΔP real,l +ΔP real,wv +ΔP real,AA-CAES );

[0169]

[0170] Where P glt represents the net load value at time t, They represent wind power and photovoltaic power output under intraday dispatching / real-time dispatching respectively. They represent the compression process and power generation process output of AA-CAES unit i under intraday scheduling / real-time scheduling respectively.

[0171] Step S232, clarifying real-time scheduling decision variables.

[0172] It is executed every 15 minutes, 96 times a day, with a resolution of 5 minutes. Using the 15-minute advance forecast data of wind, light, and load, the day-ahead thermal power start and stop plan, and the intra-day AA-CAES unit start and stop plan, and taking the intra-day thermal power and AA-CAES output plan as a reference, the wind and light output plan for the next 15 minutes and the thermal power and AA-CAES unit output correction plan are optimized.

[0173] Step S233, establishing real-time scheduling constraints.

[0174] (1) AA-CAES related constraints are on the same day.

[0175] Since the start and stop status of the AA-CAES units in the real-time dispatch model remains consistent with that of the day, the start and stop constraints are no longer considered, and the remaining AA-CAES power plant constraints are the same as before.

[0176] (2) Only the standby constraints of thermal power units are considered in the operation constraints of thermal power units.

[0177] (3) Wind and solar power output constraints are on the same day.

[0178] (4) Power balance constraints on the same day.

[0179] (5) Transmission line capacity constraints on the same day.

[0180] refer to Figure 1 , step S300, performing collaborative optimization scheduling of the power system based on the day-ahead scheduling model, the intraday scheduling model and the real-time scheduling model.

[0181] In some feasible embodiments, based on the above scheme, such as Figure 2 As shown, the step S300 includes:

[0182] Obtain the forecast data of wind, solar and load output in the day-ahead manner and input them into the day-ahead dispatch model for processing to obtain the output plan of wind, solar, thermal and AA-CAES and the start and stop plan of thermal power units in the next 24 hours;

[0183] Obtaining the intraday wind, light, and load output forecast data, and inputting the intraday wind, light, and load output forecast data, the output plans of wind, light, fire, and AA-CAES in the next 24 hours, and the start and stop plans of thermal power units into the intraday scheduling model for processing, and obtaining the wind and light output plans in the next hour, the start and stop plans of AA-CAES units, the output plans, and the intraday correction plans of thermal power unit output;

[0184] Real-time wind, solar and load output forecast data are obtained, and the real-time wind, solar and load output forecast data, the wind and solar output plan for the next hour, the AA-CAES unit start and stop, the output plan and the thermal power unit output intraday correction plan are input into the real-time scheduling model for processing to obtain the wind and solar output plan for the next 15 minutes, the thermal power unit output real-time correction plan and the energy storage unit output real-time correction plan.

[0185] In summary, the advantages of this method are:

[0186] (1) This method proposes a multi-time scale collaborative optimization scheduling of AA-CAES based on an AA-CAES type battery model suitable for power system optimization scheduling. This type of battery model takes into account the optimal efficiency external characteristics of AA-CAES and the flexible characteristics of multiple time scales such as start-stop, ramp-climbing, power, and efficiency, and can realize flexible scheduling of AA-CAES at multiple time scales.

[0187] (2) This method proposes to consider the basic requirements of the new power system and establish a reasonable AA-CAES multi-time scale collaborative optimization scheduling model for abandoned underground coal mines based on the time scale characteristics of these basic requirements, with economic efficiency as the goal on the day, low carbon as the goal within the day, and stability as the goal in real time. This method considers the multi-time scale characteristics of AA-CAES, wind and solar power, and load, as well as the time scale characteristics of each requirement under the background of the new power system.

[0188] Below, some specific implementation examples are provided.

[0189] The simulation verification is carried out with the improved IEEE33 node distribution network system. Photovoltaic power stations with capacities of 500MW, 600MW, and 500MW are connected at nodes 6, 21, and 31, respectively, and wind power stations with capacities of 800MW and 500MW are connected at nodes 15 and 23. Due to the large uncertainty of wind and light processing, thermal power generating units are connected at nodes 16, 20, and 28, and a 5000MV AA-CAES power station is connected at node 19. Figure 3 Shown is the modified IEEE33 node topology diagram.

[0190] The system parameters are shown in Table 2, and the thermal power unit parameters are shown in Table 3.

[0191] Table 2 System parameter settings

[0192]

[0193]

[0194] Table 3 Thermal power unit parameters

[0195]

[0196] Set up the following two schemes for verification:

[0197] Scheme 1: Day-ahead scheduling considering economic efficiency;

[0198] Solution 2: Consider multi-time scale optimization scheduling.

[0199] (1) The wind and solar power output of the two solutions is as follows Figure 4 and Figure 5 As shown. Figure 4 , Figure 5 It can be seen that the multi-time scale optimization scheduling method of the present invention can effectively improve the system's new energy absorption capacity and reduce the abandonment of new energy.

[0200] (2) The optimized dispatch output results of each unit in the two schemes are as follows Figure 6 and Figure 7 As shown. Figure 6 , Figure 7 It can be seen that AA-CAES is in the compression stage when the wind and solar output is large, helping to absorb new energy, and is in the power generation stage when the wind and solar output is small, helping the system to provide output to meet the load requirements. In addition, the multi-time scale collaborative optimization scheduling method of AA-CAES containing abandoned underground coal mines of the present invention can more accurately adjust the output of AA-CAES according to the changes in wind and solar output to meet the requirements of peak shaving and valley filling and new energy absorption.

[0201] (3) The economic, low-carbon and stability data of the two solutions are shown in the following table:

[0202] Table 3 Economic performance, low carbon performance and stability data

[0203]

[0204] By comparing the economic, low-carbon, and stability indicators of the two schemes, it can be seen that the method proposed by the present invention increases the peak-shaving and valley-filling benefits by 1.16 times, reduces the amount of wind and solar power abandonment to 39% of the original, reduces carbon emissions to 53% of the original, and reduces the net load variance of the power grid to 90% of the original. Therefore, compared with the day-ahead dispatch model that only considers economic efficiency, the method proposed by the present invention can greatly improve the low-carbon and stability of the system, and improve the peak-shaving and valley-filling and absorption capacity of the system.

[0205] The following describes an embodiment of the device of the present application, which can be used to implement a method for coordinated optimization and dispatching of power systems in abandoned underground coal mines in the above embodiment of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the above method of the present application.

[0206] Reference Figure 8 As shown, according to an embodiment of the present application, a power system collaborative optimization scheduling device 800 for an abandoned underground coal mine includes:

[0207] The first establishing unit 801 is used to establish a battery-like model suitable for optimal dispatching of the power system based on the gas storage pressure of the abandoned underground coal mine and the AA-CAES mathematical model;

[0208] A second establishing unit 802 is used to respectively establish a day-ahead scheduling model with economy as the goal, an intraday scheduling model with low carbon as the goal, and a real-time scheduling model with stability as the goal based on the battery-like model;

[0209] The dispatching unit 803 is used to perform coordinated optimization dispatching of the power system based on the day-ahead dispatching model, the intra-day dispatching model and the real-time dispatching model.

[0210] like Fig. 9 As shown, an embodiment of the present application also provides an electronic device 900, including a memory 910, a processor 920, and a computer program 911 stored in the memory 910 and executable on the processor. When the processor 920 executes the computer program 911, the steps of the above-mentioned method for collaborative optimization and scheduling of the power system of an abandoned underground coal mine are implemented.

[0211] Since the electronic device introduced in this embodiment is a device used to implement a collaborative optimization and dispatching device for an abandoned underground coal mine power system in the embodiment of the present application, based on the method introduced in the embodiment of the present application, technical personnel in this field can understand the specific implementation mode of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application is not introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of the present application is within the scope of protection of this application.

[0212] During the specific implementation process, when the computer program 911 is executed by a processor, any implementation method in the embodiments corresponding to the first aspect can be implemented.

[0213] Fig.10 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown.

[0214] It should be noted that Fig.10 The computer system 1000 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0215] like Fig.10 As shown, the computer system 1000 includes a central processing unit (CPU) 1001, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1002 or the program loaded from the storage part 1008 to the random access memory (RAM) 1003, such as executing the method described in the above embodiment. In the RAM 1003, various programs and data required for system operation are also stored. The CPU 1001, the ROM 1002 and the RAM 1003 are connected to each other through the bus 1004. The input / output (I / O) interface 1005 is also connected to the bus 1004.

[0216] The following components are connected to the I / O interface 1005: an input section 1006 including a keyboard, a mouse, etc.; an output section 1007 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the I / O interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1010 as needed, so that a computer program read therefrom is installed into the storage section 1008 as needed.

[0217] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part 1009, and / or installed from a removable medium 1011. When the computer program is executed by a central processing unit (CPU) 1001, various functions defined in the system of the present application are executed.

[0218] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, - but not limited to - an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by an instruction execution system, device or device or used in combination with it. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0219] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0220] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0221] As another aspect, the present application further provides a computer program product or a computer program, the computer program product or the computer program including computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method for coordinated optimization and scheduling of a power system of an abandoned underground coal mine described in the above embodiment.

[0222] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the method for coordinated optimization and scheduling of power systems in abandoned underground coal mines described in the above embodiment.

[0223] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0224] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation methods of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation methods of the present application.

[0225] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary techniques in the art that are not disclosed in the present application. It should be understood that the present application is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for coordinated optimization and dispatching of power systems in abandoned underground coal mines, characterized in that: include: A battery-like model suitable for optimal dispatching of power systems is established based on the gas storage pressure of abandoned underground coal mines and the AA-CAES mathematical model; Based on the battery-like model, a day-ahead dispatch model with economy as the goal, an intraday dispatch model with low carbon as the goal, and a real-time dispatch model with stability as the goal are respectively constructed; The power system is coordinated and optimized based on the day-ahead dispatch model, the intraday dispatch model and the real-time dispatch model.

2. The method according to claim 1, characterized in that The battery-like model suitable for power system optimization scheduling is established based on the gas storage pressure of the abandoned underground coal mine and the AA-CAES mathematical model, including: Establish a simplified AA-CAES mathematical model; Normalize the gas storage pressure; The battery model is derived based on the simplified AA-CAES mathematical model and the normalized processing results.

3. The method according to claim 2, characterized in that Based on the battery model, a day-ahead dispatch model with economy as the goal is built, including: The day-ahead dispatch objective function is established with the economic efficiency of the power system as the goal. The day-ahead dispatch objective function includes: AA-CAES daily equivalent investment cost, thermal power unit operation and maintenance cost and start-up and shutdown cost, AA-CAES unit operation and maintenance cost and start-up and shutdown cost, wind and solar operation and maintenance cost and wind and solar abandonment cost, network loss cost and electricity sales and purchase cost; Determine the day-ahead scheduling decision variables; The day-ahead dispatch constraints are established based on the battery-like model, wherein the day-ahead dispatch constraints include: AA-CAES related constraints, thermal power unit operation constraints, wind and solar power output constraints, power balance constraints and transmission line capacity constraints.

4. The method according to claim 2, characterized in that: Based on the battery-like model, a daily scheduling model with low carbon as the goal is built, including: An intraday dispatch objective function is established with the low carbon nature of the power system as the goal, and the intraday dispatch objective function includes the amount of wind and solar power abandoned and carbon emissions; Determine intraday scheduling decision variables; Based on the battery-like model, intraday scheduling constraints are established, wherein the intraday scheduling constraints include: AA-CAES related constraints, thermal power unit operation constraints, wind and solar output constraints, power balance constraints and transmission line capacity constraints.

5. The method according to claim 2, characterized in that: Based on the battery model, a real-time scheduling model with stability as the goal is built, including: A real-time dispatch objective function is established with the stability of the power system as the goal. The real-time dispatch objective function includes the net load variance of the power grid, the wind and solar power output deviation, and the AA-CAES output deviation; Determine real-time scheduling decision variables; Real-time scheduling constraints are established based on the battery-like model, wherein the real-time scheduling constraints include: AA-CAES related constraints, thermal power unit standby constraints, wind and solar power output constraints, power balance constraints and transmission line capacity constraints.

6. The method according to claim 1, characterized in that The performing coordinated optimization dispatch of the power system based on the day-ahead dispatch model, the intraday dispatch model and the real-time dispatch model includes: Obtain the forecast data of wind, solar and load output in the day-ahead manner and input them into the day-ahead dispatch model for processing to obtain the output plan of wind, solar, thermal and AA-CAES and the start and stop plan of thermal power units in the next 24 hours; Obtaining the intraday wind, light, and load output forecast data, and inputting the intraday wind, light, and load output forecast data, the output plans of wind, light, fire, and AA-CAES in the next 24 hours, and the start and stop plans of thermal power units into the intraday scheduling model for processing, and obtaining the wind and light output plans in the next hour, the start and stop plans of AA-CAES units, the output plans, and the intraday correction plans of thermal power unit output; Real-time wind, solar and load output forecast data are obtained, and the real-time wind, solar and load output forecast data, the wind and solar output plan for the next hour, the AA-CAES unit start and stop, the output plan and the thermal power unit output intraday correction plan are input into the real-time scheduling model for processing to obtain the wind and solar output plan for the next 15 minutes, the thermal power unit output real-time correction plan and the energy storage unit output real-time correction plan.

7. A coordinated optimization dispatching device for an abandoned underground coal mine power system, characterized in that: include: The first establishment unit is used to establish a battery-like model suitable for optimal dispatching of the power system based on the gas storage pressure of the abandoned underground coal mine and the AA-CAES mathematical model; A second establishment unit is used to respectively establish a day-ahead dispatch model with economy as the goal, an intraday dispatch model with low carbon as the goal, and a real-time dispatch model with stability as the goal based on the battery-like model; A dispatching unit is used to perform coordinated optimization dispatching of the power system based on the day-ahead dispatching model, the intraday dispatching model and the real-time dispatching model.

8. A computer-readable storage medium, characterized in that: The storage medium stores computer instructions, and when the computer instructions are executed on a computer, the computer is enabled to execute the method according to any one of claims 1 to 6.

9. An electronic device, characterized in that: include: Memory and processor; The memory is used to store computer instructions; The processor is used to call the computer instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-6.

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