Power system optimization method considering silicon carbide high-energy-consumption load regulation and deep peak regulation of carbon capture unit

Abstract

The invention relates to the technical field of power system optimization operation, in particular to a power system optimization method considering silicon carbide high-energy-consumption load regulation and deep peak regulation of a carbon capture unit, and the method comprises the steps: obtaining working parameters of a power system; the working parameters are input into a constructed double-layer optimization model, a scheduling optimization result of the power system is obtained, the double-layer optimization model comprises an upper-layer optimization model and a lower-layer optimization model, the upper-layer optimization model is used for optimizing a demand side conventional load curve of the power system by using price demand response, and the lower-layer optimization model is used for optimizing a demand side conventional load curve of the power system by using price demand response; and the lower-layer optimization model is used for carrying out coordinated optimization on the power system by analyzing the regulation potentials of the carbon capture unit and the silicon carbide high-energy-consumption load. The problem of renewable energy grid connection and the problem of insufficient operation flexibility of a power system caused by insufficient flexible regulation and control resources in an existing traditional scheduling mode can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization operation, and in particular to a power system optimization method taking into account silicon carbide high energy consumption load regulation and deep peak regulation of carbon capture units. Background Art

[0002] At present, there have been relevant studies at home and abroad on the combined peak-shaving of "source, load and storage", but there is still the problem of large-scale renewable energy grid connection caused by insufficient controllable resources in the power system.

[0003] At present, the research on load-side dispatching resources is focused on some high-energy-consuming resources such as electrolytic aluminum and fused magnesium. The production of silicon carbide industry is mainly carried out through the Acheson process, which processes silica sand and graphite at about 2200°C. The required heat is provided by electrode heating. The production process consumes a lot of electricity. The power of the silicon carbide furnace can be changed by adjusting the current. Therefore, the silicon carbide load has high-energy-consuming and adjustable characteristics. Silicon carbide load resources are widely distributed, concentrated in Jiangsu, Zhejiang, Shanghai and the southeast coastal areas. Because they do not have high production continuity during the production process, the implementation of interruptions has little effect on production, and a single adjustment can produce a huge step amount. There is currently very little research on the participation of silicon carbide high-energy-consuming resources in power grid dispatching, which has great research value. Therefore, the present invention proposes a power system optimization method that takes into account the regulation of silicon carbide high-energy-consuming loads and deep peak regulation of carbon capture units. Summary of the invention

[0004] The purpose of the present invention is to provide a power system optimization method taking into account the high-energy consumption load regulation of silicon carbide and the deep peak regulation of carbon capture units, so as to solve the problems of renewable energy grid connection and insufficient flexibility of power system operation caused by insufficient flexible regulation resources under the existing traditional dispatching mode.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] The power system optimization method considering the high energy consumption load regulation of silicon carbide and deep peak regulation of carbon capture units includes:

[0007] Obtaining the operating parameters of the power system;

[0008] The working parameters are input into the constructed two-layer optimization model to obtain the dispatch optimization result of the power system, wherein the two-layer optimization model includes an upper optimization model and a lower optimization model, the upper optimization model is used to optimize the conventional load curve on the demand side of the power system by utilizing price demand response, and the lower optimization model is used to coordinate and optimize the power system by analyzing the regulation potential of the carbon capture unit and the silicon carbide high energy consumption load.

[0009] Optionally, the upper optimization model takes the minimum standard deviation of conventional load as the objective function, and the objective function of the upper optimization model is:

[0010]

[0011] Among them, f1 is the objective function of the upper optimization model; P L,t is the conventional load before response in period t, and T is the scheduling period.

[0012] Optionally, the method for the upper optimization model to optimize the conventional load curve on the demand side of the power system by using the price demand response is:

[0013]

[0014] P L,2,t =P L,t +Δq t P L,t ;

[0015] Among them, ε is the demand price elasticity coefficient; p and q are the electricity price and quantity before the response respectively; Δq and Δp are the changes in quantity and price respectively; E is the price elasticity matrix; ε mm , ε mn are the self-elastic coefficient and mutual elastic coefficient, P L,t is the normal load before the response in period t, Δq t is the amount of power change in period t, P L,2,t is the normal load after response in period t.

[0016] Optionally, after the upper optimization model optimizes the conventional load curve on the demand side of the power system using the price demand response, the electricity consumption mode satisfaction and electricity cost satisfaction are used as evaluation indicators of the optimized conventional load curve on the demand side, and the evaluation indicators are:

[0017]

[0018] in, are the minimum satisfaction of electricity usage mode and electricity cost respectively; p 1,t 、p 2,t are the electricity prices in time period t before and after the response respectively; Δt is the time interval between adjacent time periods.

[0019] Optionally, the lower optimization model takes minimizing the peak-shaving cost of the power system as an objective function, and the objective function of the lower optimization model is:

[0020]

[0021]

[0022] Among them, f2 is the objective function of the lower optimization model; C mh is the coal burning cost; C rs is the solvent loss cost of the carbon capture unit; C zj Depreciation cost of carbon capture equipment; C bc Deep peak load compensation income; C qt is the start-up and shutdown cost of thermal power units; C qc The cost of wind and solar power abandonment; C yx is the sum of the operating costs of wind power and photovoltaic power; C tj Carbon trading income; C m Compensation income for silicon carbide load regulation; C cn is the cost of energy storage power station; i,t is the start and stop status of the i-th thermal power unit in period t; a i , b i 、c i is the coal consumption coefficient of the i-th thermal power unit; P G,i,t is the grid-connected power of the i-th thermal power unit in period t; N G is the number of thermal power units; H qt,i The start-up and shutdown cost of the i-th thermal power unit; is the unit carbon trading price; d i is the carbon emission coefficient of the i-th thermal power unit; e b is the carbon quota coefficient; E co2,t is the total amount of CO2 captured during period t; H qf H is the penalty price for unit wind curtailment; qv H is the penalty price for abandoning light; w,yx is the unit wind power operation price; H v,yx is the unit photovoltaic operation price; is the total wind power forecast in period t; P w,t , P w,2,t are the power of wind farm 1 and wind farm 2 in period t respectively; is the predicted photovoltaic power in period t; P v,t is the photoelectric power in period t; H up , H down They are the response subsidy costs for a single increase and decrease in SiC load; K is the state variable of the silicon carbide furnace adjusted upward during period t; rs is the cost coefficient of ethanolamine; ω rs is the solvent loss coefficient during deep peak regulation; C SJ is the total price of the carbon capture unit equipment minus the liquid storage tank; Z is the depreciation rate; P CYG is the price of the liquid storage tank per unit volume; V CYG is the volume of the liquid storage tank; NSJ The depreciation period of the carbon capture unit equipment excluding the liquid storage tank; N CYG is the depreciation period of the liquid storage tank; d is the depth peak regulation compensation coefficient; P CJ,t is the grid-connected power of the carbon capture unit at time t; η D is the energy storage discharge efficiency; P C (t), P D (t) are the charging and discharging power of the energy storage station in period t; P price,t It is the real-time peak and valley electricity price of the power grid.

[0023] Optionally, the lower-level optimization model coordinates and optimizes the power system by analyzing the regulation potential of the carbon capture unit and the silicon carbide high-energy-consuming load, and the constraints include: power system power balance constraints, thermal power unit output constraints, thermal power unit climbing constraints, thermal power unit start and stop time constraints, wind farm output constraints, photovoltaic farm output constraints, rotating standby constraints, energy storage power station constraints, silicon carbide load regulation constraints, wherein the silicon carbide load regulation constraints include regulation power constraints, state uniqueness constraints, regulation number constraints, continuous regulation time constraints, climbing constraints, and output constraints.

[0024] Optionally, the adjustment power constraint is:

[0025]

[0026] P m_min ≤P m (t)≤P m_max ;

[0027] The unique constraints of the state are:

[0028]

[0029] The adjustment times constraint is:

[0030]

[0031] The continuous adjustment time constraint is:

[0032]

[0033] The climbing constraint is:

[0034]

[0035] The output constraint is:

[0036]

[0037] Among them, P m,tis the operating power of the silicon carbide load after adjustment in period t; is the reference power of the silicon carbide load in period t; N(t) is the number of silicon carbide furnaces participating in the scheduling in period t; P up , P down They are to adjust the capacity of a single silicon carbide furnace upwards and downwards; are the state variables of the silicon carbide furnace during the period t, respectively; P m_min , P m_max They are the minimum and maximum operating power of the silicon carbide furnace under the premise of ensuring safety; The silicon carbide furnace is in the power maintenance state during the t period; M is the maximum number of adjustments for a single silicon carbide furnace; T up-max , T down_max are the maximum time for continuous upward and downward adjustment of a single silicon carbide furnace; τ is the continuous adjustment time; ΔP m (t) is the power change of the silicon carbide load from time t-1 to time t. When the value is positive, it represents upward regulation; R up , R down Respectively represent the upward and downward unit adjustment speed; λ i are the production efficiency when increasing, decreasing and maintaining power respectively; N m is the rated output of silicon carbide load during the scheduling period.

[0038] Optionally, the power balance constraint of the power system is:

[0039] The output constraint of the thermal power unit is:

[0040]

[0041] The climbing constraint of the thermal power unit is:

[0042]

[0043] The start and stop time constraints of the thermal power unit are:

[0044]

[0045] The wind farm output constraint is:

[0046]

[0047] The photovoltaic power output constraint is:

[0048]

[0049] The spinning reserve constraints are:

[0050]

[0051] The energy storage power station constraints are:

[0052]

[0053] Among them, P CN,t is the charging and discharging power of the energy storage power station during period t; are the upper and lower limits of the power of the i-th thermal power unit respectively; are the upward and downward flexibility supply of the i-th thermal power unit in period t, respectively; are the minimum startup and shutdown time of the i-th thermal power unit respectively; are the continuous start-up and shutdown time of the i-th thermal power unit respectively; are the predicted power of wind farm 1 and wind farm 2 in period t respectively; are the minimum upward and downward reserve capacities required by the system in period t, respectively; η C , η D are the energy storage charging and discharging efficiency respectively; P C (t), P D (t) are the charging and discharging power of the energy storage station in period t; Q c (t), Q d (t) are the charge and discharge signs of the energy storage in the t period, both are 0-1 variables; SOC max , SOC min are the maximum and minimum states of charge of energy storage, respectively; SOC(t) is the state of charge of energy storage in period t; P CN,max It is the maximum value of energy storage charging and discharging power.

[0054] The beneficial effects of the present invention are:

[0055] The double-layer optimization model proposed in the present invention optimizes the deep peak-shaving of carbon capture units by taking into account the regulation of high-energy-consuming silicon carbide loads and the deep peak-shaving of carbon capture units. When resources such as wind and solar power are connected to the grid on a large scale but the power regulation capacity on the source side is insufficient, the high-energy-consuming silicon carbide loads can be used as dispatchable resources, so as to timely absorb blocked wind power and achieve almost full absorption of wind and solar power generation. Under the premise of ensuring good peak-shaving effects and low economic costs, the model can improve the flexibility of the power system and ensure that the power system has sufficient flexible regulation capacity when facing uncertain grid connection of wind and solar power resources and sudden load changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0057] Figure 1 A schematic diagram of the silicon carbide production principle and power grid topology according to an embodiment of the present invention;

[0058] Figure 2 A schematic diagram of an IEEE30 node according to an embodiment of the present invention;

[0059] Figure 3 The optimal output diagram of the power system according to the embodiment of the present invention;

[0060] Figure 4 This is a diagram of power system flexibility analysis according to an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments 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.

[0062] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0063] This embodiment provides a power system optimization method taking into account the regulation of high energy consumption loads of silicon carbide and deep peak regulation of carbon capture units, including:

[0064] Obtaining the operating parameters of the power system;

[0065] The working parameters are input into the constructed two-layer optimization model to obtain the dispatch optimization result of the power system, wherein the two-layer optimization model includes an upper optimization model and a lower optimization model, wherein the upper optimization model is used to optimize the conventional load curve on the demand side of the power system by utilizing price demand response, and the upper optimization model takes the minimum standard deviation of conventional load as the objective function, and also takes the satisfaction with electricity consumption mode and the satisfaction with electricity cost as the evaluation indicators of the conventional load curve on the demand side after optimization; the lower optimization model is used to coordinate and optimize the power system by analyzing the regulation potential of carbon capture units and silicon carbide high-energy-consuming loads, and the lower optimization model takes the minimum peak-shaving cost of the power system as the objective function, and takes the power balance constraint of the power system, the output constraint of the thermal power unit, the climbing constraint of the thermal power unit, the start-stop time constraint of the thermal power unit, the output constraint of the wind farm, the output constraint of the photovoltaic farm, the rotating reserve constraint, the energy storage power station constraint, and the silicon carbide load regulation constraint as the constraint conditions, wherein the silicon carbide load regulation constraint includes the regulation power constraint, the state uniqueness constraint, the regulation number constraint, the continuous regulation time constraint, the climbing constraint, and the output constraint.

[0066] Specifically, the two-layer optimization model proposed in this embodiment, by taking into account the optimization of the high-energy-consuming load control of silicon carbide and the deep peak-shaving of the carbon capture unit, can timely absorb the blocked wind power and achieve almost full absorption of wind and solar power generation when large-scale grid-connected resources such as wind and solar power are insufficient but the source-side power supply regulation capacity is insufficient. Under the premise of ensuring good peak-shaving effects and low economic costs, the model can improve the flexibility of the power system and ensure that the power system has sufficient flexible regulation capacity when facing uncertain grid-connected wind and solar power resources and sudden load changes.

[0067] Furthermore, in this embodiment, the conventional load curve is optimized through price demand response, and no optimization measures are taken for high-energy-consuming loads. This is because they usually involve high-temperature processes and continuous production lines, and it is necessary to avoid multiple power adjustments through price demand response that may cause a decrease in product quality.

[0068] The objective function of the upper optimization model in this embodiment is:

[0069]

[0070] Among them, f1 is the objective function of the upper optimization model; P L,t is the conventional load before response in period t, and T is the scheduling period.

[0071] In this embodiment, price-based demand response is a method of encouraging users to change their electricity usage patterns by optimizing the peak-valley electricity prices on the demand side. It can assist thermal power units in optimizing their peak-shaving capacity, effectively reduce peak loads and fill valleys, smooth the net load curve, and improve load-side flexibility.

[0072] In this embodiment, the upper optimization model optimizes the conventional load curve on the demand side of the power system by using the price demand response as follows:

[0073]

[0074] P L,2,t =P L,t +Δq t P L,t ;

[0075] Among them, ε is the demand price elasticity coefficient; p and q are the electricity price and quantity before the response respectively; Δq and Δp are the changes in quantity and price respectively; E is the price elasticity matrix; ε mm , ε mn are the self-elastic coefficient and mutual elastic coefficient, P L,t is the normal load before the response in period t, Δq t is the amount of power change in period t, P L,2,t is the normal load after response in period t.

[0076] In this embodiment, the total load must remain unchanged before and after the demand response. Therefore, after the upper optimization model optimizes the conventional load curve on the demand side of the power system using the price demand response, the satisfaction with the electricity consumption mode and the satisfaction with the electricity cost are used as evaluation indicators of the conventional load curve on the demand side after optimization. The evaluation indicators are:

[0077]

[0078]

[0079] in, are the minimum satisfaction of electricity usage mode and electricity cost respectively; p 1,t 、p 2,t are the electricity prices in time period t before and after the response respectively; Δt is the time interval between adjacent time periods.

[0080] Furthermore, in this embodiment, the lower optimization model takes the minimum peak-shaving cost of the power system as the objective function to coordinate and optimize the power system:

[0081]

[0082] Among them, f2 is the objective function of the lower optimization model; C mh is the coal burning cost; C rs is the solvent loss cost of the carbon capture unit; C zj Depreciation cost of carbon capture equipment; C bc Deep peak load compensation income; C qt is the start-up and shutdown cost of thermal power units; C qc The cost of wind and solar power abandonment; Cyx is the sum of the operating costs of wind power and photovoltaic power; C tj Carbon trading income; C m Compensation income for silicon carbide load regulation; C cn is the cost of energy storage power station; i,t is the start and stop status of the i-th thermal power unit in period t; a i , b i 、c i is the coal consumption coefficient of the i-th thermal power unit; P G,i,t is the grid-connected power of the i-th thermal power unit in period t; N G is the number of thermal power units; H qt,i The start-up and shutdown cost of the i-th thermal power unit; is the unit carbon trading price; d i is the carbon emission coefficient of the i-th thermal power unit; e b is the carbon quota coefficient; E co2,t is the total amount of CO2 captured during period t; H qf H is the penalty price for unit wind curtailment; qv H is the penalty price for abandoning light; w,yx is the unit wind power operation price; H v,yx is the unit photovoltaic operation price; is the total wind power forecast in period t; P w,t , P w,2,t are the power of wind farm 1 and wind farm 2 in period t respectively; is the predicted photovoltaic power in period t; P v,t is the photoelectric power in period t; H up , H down They are the response subsidy costs for a single increase and decrease in SiC load; K is the state variable of the silicon carbide furnace adjusted upward during period t; rs is the cost coefficient of ethanolamine; ω rs is the solvent loss coefficient during deep peak regulation; C SJ is the total price of the carbon capture unit equipment minus the liquid storage tank; Z is the depreciation rate; P CYG is the price of the liquid storage tank per unit volume; V CYG is the volume of the liquid storage tank; N SJ The depreciation period of the carbon capture unit equipment excluding the liquid storage tank; N CYG is the depreciation period of the liquid storage tank; d is the depth peak regulation compensation coefficient; P CJ,t is the grid-connected power of the carbon capture unit at time t; η D is the energy storage discharge efficiency; P C (t), P D(t) are the charging and discharging power of the energy storage station in period t; P price,t It is the real-time peak and valley electricity price of the power grid.

[0083] At the same time, the lower-level optimization model analyzes the regulation potential of carbon capture units and silicon carbide high-energy-consuming loads to coordinate and optimize the power system. The constraints that need to be met include: power system power balance constraints, thermal power unit output constraints, thermal power unit climbing constraints, thermal power unit start and stop time constraints, wind farm output constraints, photovoltaic farm output constraints, rotating standby constraints, energy storage power station constraints, and silicon carbide load regulation constraints. Among them, silicon carbide load regulation constraints include regulation power constraints, state uniqueness constraints, regulation number constraints, continuous regulation time constraints, climbing constraints, and output constraints.

[0084] (1) Silicon carbide load regulation constraints:

[0085] Silicon carbide high-energy load has a large regulation capacity, which can quickly adjust power consumption in a short period of time and effectively respond to sudden imbalances in supply and demand in the power system. In the actual production process, this type of factory has a high level of automation, can quickly respond to the dispatch instructions of the power system, realize rapid power regulation, and locally consume blocked renewable energy. The production regulation principle of silicon carbide high-energy factory and its grid topology diagram for auxiliary power system peak regulation are shown in the figure below. Figure 1 shown.

[0086] (1.1) Adjust power constraints:

[0087] During the operation of the silicon carbide furnace, the safety of the operators must be ensured, so the danger caused by excessive power must be avoided. At the same time, in order to ensure the output of silicon carbide, the power cannot be too small. The power constraint is adjusted as follows:

[0088]

[0089] P m_min ≤P m (t)≤P m_max ;

[0090] (1.2) Status unique constraint:

[0091] For silicon carbide load production, there are three power regulation states: up, hold and down. The unique constraints are:

[0092]

[0093] (1.3) Adjustment times constraint:

[0094] In a scheduling cycle, the number of adjustments of a single silicon carbide furnace cannot exceed the maximum value M to ensure the quality of production products. The number of adjustments is constrained as follows:

[0095]

[0096] (1.4) Continuous adjustment time constraints:

[0097] The power of a single silicon carbide furnace cannot be adjusted up or down in a continuous period of time. Continuous adjustment will affect the temperature in the furnace, causing safety problems and affecting product quality. The continuous adjustment time constraint is:

[0098]

[0099] (1.5) Climbing constraint:

[0100]

[0101] (1.6) Output constraints:

[0102] To ensure that the output of silicon carbide high energy consumption load remains unchanged before and after adjustment, the output constraint is:

[0103]

[0104] Among them, P m,t is the operating power of the silicon carbide load after adjustment in period t; is the reference power of the silicon carbide load in period t; N(t) is the number of silicon carbide furnaces participating in the scheduling in period t; P up , P down They are to adjust the capacity of a single silicon carbide furnace upwards and downwards; are the state variables of the silicon carbide furnace during the period t, respectively; P m_min , P m_max They are the minimum and maximum operating power of the silicon carbide furnace under the premise of ensuring safety; The silicon carbide furnace is in the power maintenance state during the t period; M is the maximum number of adjustments for a single silicon carbide furnace; T up-max , T down_max are the maximum time for continuous upward and downward adjustment of a single silicon carbide furnace; τ is the continuous adjustment time; ΔP m (t) is the power change of the silicon carbide load from time t-1 to time t. When the value is positive, it represents upward regulation; R up , R down Respectively represent the upward and downward unit adjustment speed; λ i are the production efficiency when increasing, decreasing and maintaining power respectively; N m is the rated output of silicon carbide load during the scheduling period.

[0105] (2) Power system power balance constraints:

[0106]

[0107] (3) Output constraints of thermal power units:

[0108]

[0109] (4) Climbing constraints of thermal power units:

[0110]

[0111] (5) Start and stop time constraints for thermal power units:

[0112]

[0113] (6) Wind farm output constraints:

[0114]

[0115] (7) Photoelectric field output constraints:

[0116]

[0117] (8) Spinning reserve constraints:

[0118] In order to ensure the balance and stability of the power system, this embodiment adopts high-carbon thermal power units and carbon capture units to jointly bear the system's spinning reserve capacity, and the spinning reserve constraint is:

[0119]

[0120] (9) The constraints of the energy storage power station are:

[0121]

[0122] Among them, P CN,t is the charging and discharging power of the energy storage power station during period t; are the upper and lower limits of the power of the i-th thermal power unit respectively; are the upward and downward flexibility supply of the i-th thermal power unit in period t, respectively; are the minimum startup and shutdown time of the i-th thermal power unit respectively; are the continuous start-up and shutdown time of the i-th thermal power unit respectively; are the predicted power of wind farm 1 and wind farm 2 in period t respectively; are the minimum upward and downward reserve capacities required by the system in period t, respectively; η C , η D are the energy storage charging and discharging efficiency respectively; P C (t), P D (t) are the charging and discharging power of the energy storage station in period t; Qc (t), Q d (t) are the charge and discharge signs of the energy storage in the t period, both are 0-1 variables; SOC max , SOC min are the maximum and minimum states of charge of energy storage, respectively; SOC(t) is the state of charge of energy storage in period t; P CN,max It is the maximum value of energy storage charging and discharging power.

[0123] The following uses the IEEE 30 node system diagram to analyze the optimization method and double-layer optimization model proposed in this embodiment. The specific structure of the access nodes of each unit, wind farm, photovoltaic farm, etc. is detailed in Figure 2 The system parameters are detailed in Table 1-Table 5.

[0124] Table 1

[0125]

[0126] Table 2

[0127]

[0128] Table 3

[0129]

[0130] Table 4

[0131]

[0132] Table 5

[0133]

[0134]

[0135] Based on the test of the above-mentioned power system, the double-layer optimization model proposed in this embodiment, which takes into account the regulation of silicon carbide high-energy-consuming loads and deep peak-shaving of carbon capture units, can timely absorb blocked wind power and achieve almost full absorption of wind and solar power generation after taking silicon carbide high-energy-consuming loads as dispatchable resources when wind, solar and other resources are connected to the grid on a large scale but the source-side power supply regulation capacity is insufficient; while ensuring good peak-shaving effects and low economic costs, the model improves the flexibility of the system and ensures that the system has sufficient flexible adjustment capacity when facing uncertain wind and solar resources connected to the grid and sudden load changes.

[0136] The optimization method and the two-layer optimization model proposed in this embodiment are experimentally compared and verified as follows:

[0137] Step 1: Optimize the conventional load curve on the demand side using price demand response, optimize the peak and valley electricity prices, smooth the load curve, and analyze the high energy consumption load characteristics of silicon carbide;

[0138] Step 2: Based on the load characteristic curve of step 1, analyze the deep peak regulation capability of the carbon capture unit, explore the regulation potential of the carbon capture unit within the dispatchable range and in different time periods, and the regulation potential of the high energy consumption load of silicon carbide;

[0139] Step 3: Build a two-layer optimization model, simulate it using the MATLAB platform, and output the optimal solution.

[0140] Comparative Example 1:

[0141] In order to verify the effectiveness of the proposed two-layer optimization model considering silicon carbide load regulation and deep peak regulation of carbon capture units, a comparative analysis is conducted from the perspective of peak regulation cost. Based on MATLAB software, the CPLEX solver is called to solve and calculate the peak regulation cost under the two scenarios.

[0142] Scenario 1: The carbon capture unit is combined with energy storage to perform system peak regulation, and the silicon carbide load does not participate in system regulation.

[0143] Scenario 2: The carbon capture unit is combined with energy storage to perform system peak regulation, and the silicon carbide load participates in system regulation.

[0144] Combining the day-ahead forecast data of wind and solar power generation, the system peak regulation costs under two different scenarios are shown in Table 6.

[0145] Table 6

[0146]

[0147] Through the solution, it can be seen from the comparison of system peak-shaving operating costs under different scenarios shown in Table 6 that the total cost of scenario 2 is reduced by $9007 compared with scenario 1. After the silicon carbide high-energy-consuming load is used as a new peak-shaving resource, the system can almost completely absorb the wind and solar resources. The silicon carbide high-energy-consuming load has a power regulation function, which avoids frequent start and stop of thermal power units, reduces the operating cost of thermal power units, and realizes the flexible peak-shaving of the system. The peak-shaving cost of the system of the present invention is the lowest. The optimal plan of the system is as follows Figure 3 As shown in the figure, the system operation cost is the minimum in this case.

[0148] Comparative Example 2:

[0149] The focus is on analyzing the operational flexibility of the system within the dispatch cycle under two scenarios, and the flexibility adjustment margin is used to analyze the operational flexibility of the power system.

[0150] from Figure 4(a)-(b) show that the upward (downward) flexibility margin capacity of scenario 2 is much higher than that of scenario 1, and the system operation flexibility has been greatly improved. It can be seen that after using silicon carbide high-energy load as a scheduling resource, the power can be adjusted up or down to meet the system power balance and improve the system operation flexibility. In scenario 2, the system's flexibility adjustment margin is the most sufficient, and there is enough adjustment margin to cope with the uncertain volatility of wind and solar power generation and load, which verifies the effectiveness of the model.

[0151] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for optimizing a power system taking into account the regulation of high energy consumption loads of silicon carbide and deep peak regulation of carbon capture units, characterized in that: include: Obtaining the operating parameters of the power system; The working parameters are input into the constructed two-layer optimization model to obtain the dispatch optimization result of the power system, wherein the two-layer optimization model includes an upper optimization model and a lower optimization model, the upper optimization model is used to optimize the conventional load curve on the demand side of the power system by utilizing price demand response, and the lower optimization model is used to coordinate and optimize the power system by analyzing the regulation potential of the carbon capture unit and the silicon carbide high energy consumption load.

2. The power system optimization method according to claim 1, which takes into account the regulation of high energy consumption loads of silicon carbide and deep peak regulation of carbon capture units, is characterized in that: The upper optimization model takes the minimum standard deviation of conventional load as the objective function, and the objective function of the upper optimization model is: Among them, f1 is the objective function of the upper optimization model; P L,t is the conventional load before response in period t, and T is the scheduling period.

3. The power system optimization method according to claim 2, which takes into account the regulation of high energy consumption loads of silicon carbide and deep peak regulation of carbon capture units, is characterized in that: The method for the upper optimization model to optimize the conventional load curve on the demand side of the power system by using price demand response is: P L,2,t =P L,t +Δq t P L,t ; Among them, ε is the demand price elasticity coefficient; p and q are the electricity price and quantity before the response respectively; Δq and Δp are the changes in quantity and price respectively; E is the price elasticity matrix; ε mm , ε mn are the self-elastic coefficient and mutual elastic coefficient, P L,t is the normal load before the response in period t, Δq t is the change in power during period t, P L,2,t is the normal load after response in period t.

4. The power system optimization method according to claim 3 taking into account the regulation of high energy consumption loads of silicon carbide and deep peak regulation of carbon capture units, characterized in that: After the upper optimization model optimizes the conventional load curve on the demand side of the power system using the price demand response, the electricity consumption mode satisfaction and electricity cost satisfaction are used as evaluation indicators of the conventional load curve on the demand side after optimization. The evaluation indicators are: in, are the minimum satisfaction of electricity usage mode and electricity cost respectively; p 1,t 、p 2,t are the electricity prices in time period t before and after the response respectively; Δt is the time interval between adjacent time periods.

5. The power system optimization method according to claim 1, which takes into account the regulation of high energy consumption loads of silicon carbide and deep peak regulation of carbon capture units, is characterized in that: The objective function of the lower optimization model is to minimize the peak load cost of the power system. The objective function of the lower optimization model is: Among them, f2 is the objective function of the lower optimization model; C mh is the coal cost; C rs is the solvent loss cost of the carbon capture unit; C zj Depreciation cost of carbon capture equipment; C bc Deep peak load compensation income; C qt is the start-up and shutdown cost of thermal power units; C qc The cost of wind and solar power abandonment; C yx is the sum of the operating costs of wind power and photovoltaic power; C tj Carbon trading income; C m Compensation income for silicon carbide load regulation; C cn is the cost of energy storage power station; i,t is the start and stop status of the i-th thermal power unit in period t; a i , b i 、c i is the coal consumption coefficient of the i-th thermal power unit; P G,i,t is the grid-connected power of the i-th thermal power unit in period t; N G is the number of thermal power units; H qt,i The start-up and shutdown cost of the i-th thermal power unit; H CO2 is the unit carbon trading price; d i is the carbon emission coefficient of the i-th thermal power unit; e b is the carbon quota coefficient; E co2,t is the total amount of CO2 captured during period t; H qf H is the penalty price for unit wind curtailment; qv H is the penalty price for abandoning light; w,yx is the unit wind power operation price; H v,yx is the unit photovoltaic operation price; is the total wind power forecast in period t; P w,t , P w,2,t are the power of wind farm 1 and wind farm 2 in period t respectively; is the predicted photovoltaic power in period t; P v,t is the photoelectric power in period t; H up , H down They are the response subsidy costs for a single increase and decrease in SiC load; K is the state variable of the silicon carbide furnace adjusted upward during period t; rs is the cost coefficient of ethanolamine; ω rs is the solvent loss coefficient during deep peak regulation; C SJ is the total price of the carbon capture unit equipment minus the liquid storage tank; Z is the depreciation rate; P CYG is the price of the liquid storage tank per unit volume; V CYG is the volume of the liquid storage tank; N SJ The depreciation period of the carbon capture unit equipment excluding the liquid storage tank; N CYG is the depreciation period of the liquid storage tank; d is the depth peak regulation compensation coefficient; P CJ,t is the grid-connected power of the carbon capture unit at time t; η D is the energy storage discharge efficiency; P C (t), P D (t) are the charging and discharging power of the energy storage station in period t; P price,t It is the real-time peak and valley electricity price of the power grid.

6. The power system optimization method according to claim 5 taking into account the regulation of high energy consumption loads of silicon carbide and deep peak regulation of carbon capture units, characterized in that: The lower-level optimization model coordinates and optimizes the power system by analyzing the regulation potential of carbon capture units and silicon carbide high-energy-consuming loads. The constraints include: power system power balance constraints, thermal power unit output constraints, thermal power unit climbing constraints, thermal power unit start and stop time constraints, wind farm output constraints, photovoltaic farm output constraints, rotating standby constraints, energy storage power station constraints, and silicon carbide load regulation constraints. The silicon carbide load regulation constraints include regulation power constraints, state uniqueness constraints, regulation number constraints, continuous regulation time constraints, climbing constraints, and output constraints.

7. The power system optimization method according to claim 6, which takes into account the regulation of high energy consumption loads of silicon carbide and deep peak regulation of carbon capture units, is characterized in that: The adjustment power constraint is: P m_min ≤P m (t)≤P m_max ; The unique constraints of the state are: The adjustment times constraint is: The continuous adjustment time constraint is: The climbing constraint is: The output constraint is: Among them, P m,t is the operating power of the silicon carbide load after adjustment in period t; is the reference power of the silicon carbide load in period t; N(t) is the number of silicon carbide furnaces participating in the scheduling in period t; P up , P down They are to adjust the capacity of a single silicon carbide furnace upwards and downwards; are the state variables of the silicon carbide furnace during the period t, respectively; P m_min , P m_max They are the minimum and maximum operating power of the silicon carbide furnace under the premise of ensuring safety; The silicon carbide furnace is in the power maintenance state during the t period; M is the maximum number of adjustments for a single silicon carbide furnace; T up-max , T down_max are the maximum time for continuous upward and downward adjustment of a single silicon carbide furnace; τ is the continuous adjustment time; ΔP m (t) is the power change of the silicon carbide load from time t-1 to time t. When the value is positive, it represents upward regulation; R up , R down Respectively represent the upward and downward unit adjustment speed; λ i are the production efficiency when increasing, decreasing and maintaining power respectively; N m is the rated output of silicon carbide load during the scheduling period.

8. The power system optimization method according to claim 7, taking into account the regulation of high energy consumption loads of silicon carbide and deep peak regulation of carbon capture units, is characterized in that: The power balance constraint of the power system is: The output constraint of the thermal power unit is: The climbing constraint of the thermal power unit is: The start and stop time constraints of the thermal power unit are: The wind farm output constraint is: The photovoltaic power output constraint is: The spinning reserve constraints are: The energy storage power station constraints are: Among them, P CN,t is the charging and discharging power of the energy storage power station during period t; are the upper and lower limits of the power of the i-th thermal power unit respectively; are the upward and downward flexibility supply of the i-th thermal power unit in period t, respectively; are the minimum startup and shutdown time of the i-th thermal power unit respectively; are the continuous start-up and shutdown time of the i-th thermal power unit respectively; are the predicted power of wind farm 1 and wind farm 2 in period t respectively; are the minimum upward and downward reserve capacities required by the system in period t, respectively; η C , η D are the energy storage charging and discharging efficiency respectively; P C (t), P D (t) are the charging and discharging power of the energy storage station in period t; Q c (t), Q d (t) are the charge and discharge signs of the energy storage in the t period, both are 0-1 variables; SOC max , SOC min are the maximum and minimum states of charge of energy storage, respectively; SOC(t) is the state of charge of energy storage in period t; P CN,max It is the maximum value of energy storage charging and discharging power.

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