High-proportion new energy consumption-oriented fused magnesium load cooperative scheduling method

Abstract

The invention relates to a high-proportion new energy consumption-oriented fused magnesium load cooperative scheduling method, which comprises the following steps of: acquiring related parameters of a power system, including a generator power parameter, a new energy power generation prediction parameter and a conventional load prediction parameter; establishing a fused magnesium load model considering strong process constraints; constructing a double-layer optimization scheduling model considering the electric smelting magnesium load; and based on the related parameters of the electric power system and the fused magnesium load model, solving the double-layer optimization scheduling model through a solver CPLEX to obtain the active power and fused magnesium load power of all thermal power generating units in each time period, and scheduling the thermal power generating units and the fused magnesium furnace in the electric power system. According to the scheduling method, the influence of the uncertainty of wind and light output is fully considered, the fused magnesium load characteristics are deeply considered, the double-layer optimization scheduling result is quickly and effectively obtained, and the scheduling precision and speed of the generator set and the fused magnesium load are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system dispatching, and relates to a collaborative dispatching method for electrofused magnesia load for high-proportion new energy consumption. Background Art

[0002] With the intensification of the global energy crisis and the increasing environmental protection requirements, the development and application of new energy have become the common goal of countries around the world. The grid connection of a large number of renewable energy sources poses huge challenges to the power grid. In particular, the phenomena of wind curtailment and light curtailment are serious, affecting the operation stability and economy of the power system. The peak shaving function of traditional thermal power units can no longer meet the growing demand, and the flexibility and regulation ability of the power grid need to be improved urgently. Therefore, how to effectively consume high-proportion new energy has become an important topic in power system research.

[0003] In recent years, with the gradual attention to load-side resources, flexible and adjustable high-energy-consuming loads have gradually become an important regulation means in power grid dispatching. By introducing high-energy-consuming loads into power grid dispatching, the load fluctuations of traditional generating units can be reduced, and the regulation ability and stability of the power grid can be improved, especially in the context of high-proportion new energy grid connection. As an important form of high-energy-consuming load, electrofused magnesia has significant regulation potential and can play an important role in power grid dispatching. At present, various dispatching methods have been proposed at home and abroad, such as strategies based on multi-objective optimization, demand response, and load-side management, to improve the peak shaving ability and new energy consumption ability of the system. However, the research on high-energy-consuming loads, especially the participation of electrofused magnesia load in power grid dispatching, is still relatively scarce. The regulation ability of electrofused magnesia load is limited by its production process and equipment characteristics, and there are still certain challenges in fully exploring its regulation ability. To effectively solve these problems, it is necessary to deeply study the dispatching method of electrofused magnesia load, and design an optimized dispatching strategy in combination with the grid demand and the actual situation of the power generation side to maximize the overall benefit of the system. Summary of the Invention

[0004] To solve the above technical problems, the purpose of the present invention is to provide a collaborative dispatching method for electrofused magnesia load for high-proportion new energy consumption.

[0005] The present invention provides a collaborative dispatching method for electrofused magnesia load for high-proportion new energy consumption, including:

[0006] Step 1: Obtain relevant parameters of the power system, including generator power parameters, new energy power generation prediction parameters, and conventional load prediction parameters;

[0007] Step 2: Establish an electrofused magnesia load model considering strong process constraints;

[0008] Step 3: Construct a two-layer optimal dispatching model considering electrofused magnesia load;

[0009] Step 4: Based on the relevant parameters of the power system and the electrofused magnesia load model, solve the two-layer optimal scheduling model through the solver CPLEX to obtain the active power of each thermal power unit and the electrofused magnesia load power at each time period, and schedule the thermal power units and electrofused magnesia furnaces in the power system.

[0010] A collaborative scheduling method for electrofused magnesia load for high-proportion new energy consumption in the present invention takes the high-energy-consuming load of electrofused magnesia on the demand side as the regulation object to jointly participate in the system optimal scheduling with thermal power units, and establishes a two-layer optimization model for electrofused magnesia high-energy-consuming load considering strong process constraints for high-proportion new energy consumption, having the following beneficial effects:

[0011] (1) The present invention analyzes the adjustable resource characteristics of the electrofused magnesia production process flow and establishes a mathematical model describing its strong constraints in production work. The proposed optimization model can effectively improve the peak regulation flexibility of the system, reduce the system operation cost, and provide strong support for the construction of a new power system.

[0012] (2) By adopting the two-layer optimal scheduling model considering electrofused magnesia load proposed by the present invention, compared with the traditional electrofused magnesia park operation model, the amount of abandoned wind and light is significantly reduced, the new energy consumption level is significantly improved, and the system's absorption capacity for high-proportion new energy is further enhanced.

[0013] (3) In the case of high-proportion new energy grid connection and insufficient regulation ability of conventional thermal power units, the present invention integrates the high-energy-consuming load of electrofused magnesia as a regulation means into the system optimal scheduling, comprehensively considers the impact of source-load coordination on wind and light consumption and economic operation, and realizes minimizing the system operation cost while ensuring maximizing the consumption of high-proportion new energy. Brief Description of the Drawings

[0014] Figure 1 is the flow chart of a collaborative scheduling method for electrofused magnesia load for high-proportion new energy consumption in the present invention;

[0015] Figure 2 is the schematic diagram of the material layer of the electrofused magnesia furnace in the embodiment of the present invention;

[0016] Figure 3 is the electrothermal conversion circuit diagram of the electrofused magnesia furnace in the embodiment of the present invention;

[0017] Figure 4 is the schematic diagram of the simplified single-phase equivalent circuit in the embodiment of the present invention;

[0018] Figure 5 is the schematic diagram of the solution process of the two-layer optimization model in the embodiment of the present invention. Detailed Embodiment

[0019] AsFigure 1 As shown in the figure, a collaborative scheduling method for electrofused magnesia load for high proportion of new energy consumption in the present invention includes:

[0020] Step 1: Obtain relevant parameters of the power system, including generator power parameters, new energy power generation prediction parameters, and conventional load prediction parameters.

[0021] The generator power parameters include: the number of thermal power units, the capacity of thermal power units, the power generation cost of a single generating unit, and the down-ramp rate vector and up-ramp rate vector of thermal power units.

[0022] The new energy power generation prediction parameters include: wind power generation prediction parameters and photovoltaic power generation prediction parameters.

[0023] The conventional load prediction parameters include: other non-adjustable loads except electrofused magnesia load.

[0024] Step 2: Establish an electrofused magnesia load model considering strong process constraints, specifically:

[0025] Step 2.1: Determine the electrofused magnesia furnace process.

[0026] At present, the main equipment for producing electrofused magnesia in China is a three-phase electrofused magnesia furnace. During the actual smelting process, light-burned magnesia is used as the raw material, and smelting is carried out by relying on the submerged arc heat release and the resistance heat of the furnace charge during the melting process of the three-phase electrodes. The strong process constraints of the electrofused magnesia furnace refer to the strict process requirements and limiting conditions faced by the electrofused magnesia furnace during production. The main component equipment of the electrofused magnesia furnace includes an electric furnace power supply system, an electrode up and down control system, a feeding system, and the electric furnace body, etc. As Figure 2 shown, there are three layers of materials in the electrofused magnesia furnace. The upper layer is a furnace charge layer with a thickness of h n and a resistivity of ρ n , and the furnace charge layer is either loose material or melt; the middle layer is a melt layer with a thickness of l and a resistivity of ρ m ; the bottom layer is a smelting product layer with good conductivity, which can be regarded as an equipotential layer and serves as the neutral point of the three-phase electricity. The end of the electrode is a hemispherical surface with a radius of r 0 , h m is the distance between two electrodes, and d is the electrode diameter.

[0027] Specifically, when implemented, the electrofused magnesia furnace described in the present invention is a three-phase electrofused magnesia furnace, and the process includes: arc heat release, resistance heat release of the furnace charge, and resistance heat release of the melt layer. As Figure 3 shown in the electrofused magnesia furnace electrothermal conversion circuit, where R a is the submerged arc resistance, R p is the resistance of the molten pool under the arc, and R nis the resistance of the charge layer between the two electrodes. To simplify the problem, assume that the three-phase power of the electrofused magnesia furnace is balanced, and the electrothermal conversion circuit in the electrofused magnesia furnace is simplified into a single-phase equivalent circuit as shown in Figure 4 The expression is:

[0028] U = I(X + R + R ia + R ip )

[0029] where U is the voltage of the single-phase equivalent circuit, I is the electrode current, R is the main circuit resistance, X is the equivalent reactance of the single-phase equivalent circuit, R ia is the submerged arc resistance, and R ip is the bath resistance.

[0030] The resistance R n of the charge layer between the two electrodes is calculated as:

[0031]

[0032] Step 2.2: Calculate the working resistance: For the electrofused magnesia furnace studied in the present invention, the charge layer is powdered electrofused magnesia with a large resistivity, and the charge layer resistance can be regarded as infinite, that is, the delta-connected circuit can be regarded as an open circuit.

[0033] The bath resistance R ip is calculated as:

[0034]

[0035] where ρ m is the resistivity of the melt, d is the electrode diameter, r 0 is the radius of the hemispherical surface at the electrode end, and l is the thickness of the melt layer.

[0036] During the production of the electrofused magnesia furnace, when the electrode immersion depth changes, l also changes accordingly. After a period of smelting process, it can be considered that l >> r 0 , that is:

[0037]

[0038] The general experimental relationship of the submerged arc resistance R ia is:

[0039]

[0040] In the formula: ρ a is the submerged arc resistivity.

[0041] Step 2.3: Assume that the three-phase electrofused magnesia furnace circuit is a three-phase balanced sinusoidal AC circuit, and the active power P O output by the power supply is:

[0042] PO = P s + P ia + P ip

[0043] wherein, P s is the main circuit loss power, P ia is the electric heat of the submerged arc resistance, and P ip is the electric heat of the molten pool resistance.

[0044] The electrode current I is:

[0045]

[0046] wherein, U 1 is the power supply voltage of the fused magnesia furnace. Substituting the electrode current I into the active power P O yields:

[0047]

[0048] Step 2.4: Load characteristics of the fused magnesia furnace.

[0049] Since the change range of R ia is too large, not taking R ia as the parameter variable, but taking the electrode current I as the independent variable, and using the curves of each quantity as functions to describe the relationship between each electric quantity, which is called the power curve diagram, describing the relationship between the arc current I and the arc power; the optimal melting current value of the magnesia furnace is I 1m = 14 kA, the optimal working upper limit current of the electrode current I is I b = 16 kA, and the optimal working lower limit current of the current I is I k = 13 kA. By organizing the electrode current I and the active power P O , the power output of the power supply is obtained:

[0050]

[0051] In the formula: I x is the different states of the electrode current, which can be the optimal melting current value I 1m , the optimal working upper limit current value I b and the optimal working lower limit current value I k ; similarly, P Ox is the active power output of the power supply for different states of I x , which can be the active power output of the power supply corresponding to the optimal melting current value I 1m , that is, the optimal melting power P O1m , the active power output of the power supply corresponding to the optimal working upper limit current value I b , the optimal melting upper limit power P Mgup,k and the optimal working lower limit current value Ik The corresponding active power output of the power supply, i.e., the optimal smelting lower limit power P Mgdown,k .

[0052] Step 3: Construct a two-layer optimal scheduling model considering the electrofused magnesia load. The specific steps of Step 3 are as follows:

[0053] Step 3.1: Construct the upper-layer optimal model considering the electrofused magnesia load. The upper-layer optimal model aims at economic optimality, and its objective function is:

[0054] minF = C G + C n

[0055]

[0056] In the formula: F is the system operation cost, C G is the operation cost of the thermal power unit, C n is the new energy power generation cost; T = 24 hours, N G is the number of thermal power units, is the start-stop status variable of thermal power unit z at time t, indicates that the unit is in the shutdown state at time t, indicates that the unit is in the startup state at time t, is the active power of thermal power unit z at time t, a z , b z , c z are the operation cost parameters of thermal power unit z; N w is the number of wind farms, N pv is the number of photovoltaic power generation fields, is the predicted active power of the wind farm at time t, is the predicted active power of the photovoltaic power generation field at time t, ρ i is the unit wind abandonment penalty cost of wind farm i, ρ j is the unit light abandonment penalty cost of photovoltaic power generation field j.

[0057] Step 3.2: Design the constraint conditions of the upper-layer optimal model;

[0058] (1) System power balance constraint condition:

[0059]

[0060] In the formula: is the active power of wind farm i at time t, is the active power of photovoltaic power generation field j at time t, is the active power of the conventional load at time t, is the active power of the electrofused magnesia furnace k during the period t, N Mg is the number of electrofused magnesia furnaces.

[0061] (2) The operating constraint conditions of thermal power units include upper and lower output power constraints and ramp rate constraints:

[0062]

[0063] In the formula: are the upper and lower limits of the output power of thermal power unit z respectively, is the active power of thermal power unit z during the period t - 1, P Gup,z 、P Gdown,z are the upward and downward ramp rates of thermal power unit z respectively.

[0064] (3) The output constraint conditions of the wind farm include power balance constraint and power constraint:

[0065]

[0066] Among them, is the active power of the wind farm during the period t; is the wind curtailment power of the wind farm during the period t.

[0067] (4) The output constraint conditions of the photovoltaic include power balance constraint and power constraint:

[0068]

[0069] Among them, is the active power of the photovoltaic unit during the period t; is the light curtailment power of the photovoltaic power generation field during the period t.

[0070] Step 3.3: Construct a lower-layer optimization model considering the electrofused magnesia load.

[0071] Using the total power of thermal power units and the power of wind and light obtained from the upper-layer optimization model, optimize the new energy utilization rate of the system. Based on the optimal scheduling plan of the upper-layer optimization model, considering the operating cost of thermal power units and the power generation cost of new energy, optimize the system with the goal of maximizing the new energy consumption of the system. The mathematical description of the lower-layer optimization model is:

[0072]

[0073] In the formula: E abon is the total power of the abandoned new energy, is the wind curtailment power of wind farm i during the period t, is the light curtailment power of photovoltaic power generation field j during the period t.

[0074] Step 3.4: Design the constraint conditions of the lower-layer optimization model:

[0075] (1) Regulation power constraint of the electrofused magnesia furnace

[0076]

[0077] In the formula: is the active power of the electrofused magnesia furnace k at time t, is the active power of the electrofused magnesia furnace k at time t-1; P Mgup,k and P Mgdown,k are the upper and lower limits of the smelting power of the electrofused magnesia furnace k, respectively, which are determined by the relationship between the electrode current and the power output of the power supply of the electrofused magnesia furnace.

[0078] (2) Upper and lower limits constraints of the output power:

[0079]

[0080] In the formula: are the upper and lower limits of the power of the electrofused magnesia furnace, respectively.

[0081] Step 4: Based on the relevant parameters of the power system and the electrofused magnesia load model, solve the two-layer optimization scheduling model through the solver CPLEX to obtain the active power of all thermal power units and the electrofused magnesia load power at each time period, and schedule the thermal power units and electrofused magnesia furnaces in the power system, as Figure 5 shown, specifically:

[0082] Step 4.1: On the basis of satisfying the constraint conditions of the upper-layer optimization model, with the goal of economic optimization, optimize the power of thermal power units, the power of wind farms, and the power of photovoltaic power plants based on the relevant parameters of the power system to obtain the active power of all thermal power units at each time period, the active power of all wind farms at each time period, the active power of all photovoltaic power plants at each time period, and the system operation cost.

[0083] Step 4.2: Use the active power of all thermal power units at each time period, the active power of all wind farms at each time period, and the active power of all photovoltaic power plants at each time period obtained from the upper-layer model. By adjusting the electrofused magnesia load power, on the basis of the optimal scheduling plan of the upper-layer optimization model, optimize the system with the goal of maximizing the new energy consumption of the system, and calculate the new energy utilization rate.

[0084] Step 4.3: When the system operation cost of the upper-layer optimization model is less than the expected value and the new energy utilization rate calculated by the lower-layer optimization model is greater than the expected value, execute Step 4.4; otherwise, return to Step 4.1.

[0085] Step 4.4: Output the active power of each thermal power unit in each period, the active power of each wind farm in each period, the active power of each photovoltaic power plant in each period, the electrofused magnesia load power, the system operation cost, and the new energy utilization rate.

[0086] Step 4.5: Based on the obtained active power of each thermal power unit in each period and the electrofused magnesia load power, schedule the thermal power units and electrofused magnesia load in the power system.

[0087] The present invention fully considers the strong process constraints of the electrofused magnesia load, deeply considers the characteristics of the electrofused magnesia load, quickly and effectively obtains the double-layer scheduling results, improves the scheduling accuracy and speed of thermal power units and electrofused magnesia load, enhances the system's ability to absorb new energy, and at the same time reduces the system operation cost, providing strong support for the construction of a new power system.

[0088] The above are only the preferred embodiments of the present invention and are not intended to limit the idea of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for coordinated dispatching of fused magnesium loads for high-proportion new energy consumption, characterized in that: include: Step 1: Obtain relevant parameters of the power system, including generator power parameters, new energy power generation forecast parameters and conventional load forecast parameters; Step 2: Establish a fused magnesium load model considering strong process constraints; Step 3: Construct a two-layer optimization scheduling model taking into account the fused magnesium load; Step 4: Based on the relevant parameters of the power system and the fused magnesium load model, the double-layer optimization scheduling model is solved by the solver CPLEX to obtain the active power and fused magnesium load power of all thermal power units in each period, and the thermal power units and fused magnesium furnaces in the power system are scheduled.

2. The method for coordinated dispatching of fused magnesium load for high proportion of new energy consumption according to claim 1, characterized in that: The generator power parameters include: the number of thermal power units, the capacity of the thermal power units, the power generation cost of a single generator unit, and the down-ramp rate vector and up-ramp rate vector of the thermal power unit; The new energy power generation prediction parameters include: wind power generation prediction parameters and photovoltaic power generation prediction parameters; The conventional load prediction parameters include: other unadjustable loads except the fused magnesium load.

3. The method for coordinated dispatching of fused magnesium load for high proportion of new energy consumption according to claim 1 is characterized in that: The step 2 is specifically as follows: Step 2.1: Determine the process of the fused magnesium furnace: There are three layers of materials in the fused magnesium furnace, the upper layer is the charge layer, the middle layer is the melt layer, and the lower layer is the smelting product layer; the fused magnesium furnace is a three-phase fused magnesium furnace, and the process includes: arc heat release, resistance heat release of the charge and resistance heat release of the melt layer; assuming that the three-phase power of the fused magnesium furnace is balanced, the electric heat conversion circuit in the fused magnesium furnace is simplified into a single-phase equivalent circuit, and the expression is: U=I(X+R+R ia +R ip ) Among them, U is the voltage of the single-phase equivalent circuit, I is the electrode current, R is the main circuit resistance, X is the equivalent reactance of the single-phase equivalent circuit, R ia is the submerged arc resistance, R ip is the molten pool resistance; Step 2.2: Calculate the working resistance: Melt pool resistance R ip The calculation formula is: Among them, ρ m is the resistivity of the melt, d is the electrode diameter, r0 is the radius of the hemispherical surface at the end of the electrode, and l is the thickness of the melt layer; When the electric magnesium furnace is in production, the electrode burying depth changes, and l also changes accordingly. After the smelting process has been going on for a period of time, it can be regarded as l>>r0, that is: Submerged arc resistance R ia The general experimental relationship is: Where: a is the submerged arc resistivity; Step 2.3: Assume that the three-phase electric magnesium furnace circuit is a three-phase balanced sinusoidal AC circuit, and the active power P output by the power supply is O for: P O =P s +P ia +P ip Among them, P s is the main circuit power loss, P ia is submerged arc resistance heating, P ip It is the resistance electric heating of the molten pool; The electrode current I is: Among them, U1 is the power supply voltage of the electric magnesium furnace, which brings the electrode current I into the active power P O get: Step 2.4: Load characteristics of fused magnesium furnace; Due to R ia The range of variation is too large to be based on R ia The power curve is a parameter, and the electrode current I is an independent variable. The curve with each quantity as a function is used to describe the relationship between each quantity. It describes the relationship between the arc current I and the arc power. The optimal melting current value of the magnesium melting furnace is I 1m =14kA, the optimal upper limit current of electrode current I is I b =16KA, the best working lower limit current of current I is I k =13kA, by sorting out the electrode current I and active power P O , and get the power supply output power: Where: I x For different states of electrode current, it can be the optimal melting current value I 1m , the best working upper limit current value I b And the best working lower limit current value I k Similarly, P Ox For I x The power output active power of the power supply at different states of electrode current can be the optimal melting current value I 1m The corresponding power output active power, that is, the optimal melting power P O1m , the best working upper limit current value I b The corresponding power output active power, the optimal smelting upper limit power P Mgup,k And the best working lower limit current value I k The corresponding power output active power, that is, the optimal smelting lower limit power P Mgdown,k .

4. The method for coordinated dispatching of fused magnesium load for high proportion of new energy consumption according to claim 3 is characterized in that: The step 3 is specifically as follows: Step 3.1: Construct an upper optimization model taking into account the fused magnesium load. The upper optimization model takes economic optimization as its goal, and its objective function is: minF=C G +C n Where: F is the system operating cost, C G is the operating cost of thermal power units, C n is the cost of renewable energy power generation; T = 24 hours, N G is the number of thermal power units, is the start-stop state variable of thermal power unit z in period t, Indicates that the unit is in shutdown state during period t. Indicates that the unit is in the on state during the t period. is the active power of thermal power unit z in period t, a z , b z 、c z is the operating cost parameter of thermal power unit z; N w is the number of wind farms, N pv is the number of photovoltaic power plants, is the predicted active power of the wind farm in period t, is the predicted active power of the photovoltaic power plant in period t, ρ i is the penalty cost of wind curtailment per unit of wind farm i, ρ j is the unit penalty cost of abandoned light in PV farm j; Step 3.2: Design the constraints of the upper optimization model; (1) System power balance constraints: Where: is the active power of wind farm i in period t, is the active power of photovoltaic power plant j in period t, is the active power of the conventional load in period t, is the active power of the fused magnesium furnace k in period t, N Mg is the number of electric fused magnesium furnaces; (2) The operating constraints of thermal power units include upper and lower limits of output power and climbing speed constraints: Where: are the upper and lower limits of the output power of thermal power unit z, is the active power of thermal power unit z in period t-1, P Gup,z , P Gdown,z are the ramp-up rate and ramp-down rate of thermal power unit z respectively; (3) Wind farm output constraints include power balance constraints and power constraints: in, is the active power of the wind farm in period t; is the abandoned wind power of the wind farm in period t; (4) PV output constraints include power balance constraints and power constraints: in, is the active power of the photovoltaic unit in period t; is the abandoned light power of the photovoltaic power plant in period t; Step 3.3: Construct the lower layer optimization model taking into account the fused magnesium load; The total power of thermal power units and wind and solar power obtained by the upper optimization model are used to optimize the utilization rate of new energy in the system. Based on the optimal dispatching scheme of the upper optimization model, the operating cost of thermal power units and the cost of new energy generation are taken into account, and the system is optimized with the goal of maximizing the system's consumption of new energy. The mathematical description of the lower optimization model is: Where: E abon is the total power of renewable energy wasted, is the abandoned wind power of wind farm i in period t, is the abandoned light power of photovoltaic power plant j in period t; Step 3.4: Design constraints for the lower-level optimization model: (1) Power regulation constraints of fused magnesium furnace Where: is the active power of the electric magnesium furnace k in period t, is the active power of the fused magnesium furnace k in the period t-1; P Mgup,k , P Mgdown,k are the upper and lower limits of the smelting power of the electric fused magnesium furnace k, respectively, which are determined by the relationship between the electrode current and the power output power of the electric fused magnesium furnace; (2) Output power upper and lower limit constraints: Where: They are the upper and lower limits of the power of the electric fused magnesium furnace respectively.

5. The method for coordinated dispatching of fused magnesium load for high proportion of new energy consumption according to claim 4, characterized in that: The step 4 is specifically as follows: Step 4.1: On the basis of satisfying the constraints of the upper optimization model, with the goal of economic optimization, the power of thermal power units, wind farm power, and photovoltaic power plant power are optimized based on the relevant parameters of the power system to obtain the active power of all thermal power units in each period, the active power of all wind farms in each period, the active power of all photovoltaic power plants in each period, and the system operation cost; Step 4.2: Using the active power of all thermal power units in each period, the active power of all wind farms in each period, and the active power of all photovoltaic power plants in each period obtained by the upper-level model, by adjusting the load power of fused magnesium, on the basis of the optimal dispatching scheme of the upper-level optimization model, the system is optimized with the goal of maximizing the system's absorption of new energy, and the utilization rate of new energy is calculated; Step 4.3: When the system operation cost of the upper optimization model is less than the expected value, and the new energy utilization rate calculated by the lower optimization model is greater than the expected value, execute step 4.4, otherwise return to step 4.1; Step 4.4: Output the active power of all thermal power units in each period, the active power of all wind farms in each period, the active power of all photovoltaic power plants in each period, the fused magnesium load power, the system operation cost and the utilization rate of new energy; Step 4.5: Based on the obtained active power and fused magnesium load power of all thermal power units in each period, the thermal power units and fused magnesium load in the power system are dispatched.

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