CN117293801B - Source-load coordination scheduling method considering fine modeling of electric arc furnace load - Google Patents
Source-load coordination scheduling method considering fine modeling of electric arc furnace load Download PDFInfo
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Abstract
The invention discloses a source load coordination scheduling method considering fine modeling of electric arc furnace load. The invention provides a new optimization method for the response of the demand side of the power system, and further improves the operation efficiency and the economy of the power system. After the high energy consumption load adjustment of adding the fused magnesium is adopted, the wind power consumption is greatly increased, the output of the thermal power unit is smooth, and the peak regulation capacity of the thermal power unit is equivalent to being increased. And through the double optimization method, the total running cost of the system can be reduced under the guarantee of improving the wind power utilization rate. The electric arc furnace can be started and stopped in a short time, so that quick adjustment is realized, and the power and load characteristics of the electric smelting magnesium load can be controlled by adjusting the factors such as the power supply current, the electrode shape and the arc stability of the electric arc furnace, namely, the high energy consumption load of the electric smelting magnesium participates in the source load joint adjustment mode, so that the wind power consumption can be effectively increased, and the running cost of a system can be reduced.
Description
Technical Field
The invention belongs to the technical field of clean energy consumption, and particularly relates to a source-load coordination scheduling method considering fine modeling of electric arc furnace load.
Background
In recent years, the problems of uncertainty in energy supply, shortage of non-renewable resources, and the like have become a common challenge worldwide. Therefore, sustainable energy and energy efficiency are currently one of the most urgent and prospective tasks to ensure energy safety. Meanwhile, the power system demand side response is used as a power regulation and control mode, and has important significance in the aspects of improving the energy utilization rate and reducing the energy consumption.
In order to solve the problems, the development and application of new energy are required to be quickened, the fourth technical revolution opportunity is captured, and the utilization rate of renewable energy is improved. Meanwhile, the construction of a modern energy system and the improvement of energy efficiency are key to solving the energy dependence and the shortage of non-renewable energy. To achieve these goals, governments and businesses need to enhance collaboration, increase technological innovation, and comprehensively increase energy management and technology levels.
Disclosure of Invention
In view of the above, the invention aims to provide a source load coordination scheduling method considering the load fine modeling of an electric arc furnace, which aims at maximizing wind power consumption and minimizing running cost, realizes the cooperative optimization among wind power, thermal power and high energy consumption load, and improves the wind power utilization rate and the system economy.
The technical scheme adopted by the invention is as follows:
the invention provides a source load coordination scheduling method considering fine modeling of electric arc furnace load, which is implemented according to the following steps:
step S1: establishing a first re-optimization model with maximum wind power consumption as an objective function after the high energy consumption load of fused magnesium is used to participate in a source load joint regulation mode;
step S2: based on the optimized wind power consumption model in the step S1, a second optimizing model which minimizes the total cost of system operation as an objective function is established;
step S3: a heuristic algorithm is selected to solve the source-load coordination scheduling optimization model;
further, the step S1 of establishing a first re-optimization model with maximum wind power consumption as an objective function after the high energy consumption load of the fused magnesium is used to participate in the source load joint adjustment mode includes:
on the basis of meeting various constraint conditions such as thermal power generating units, wind power, loads and the like, taking the maximum wind power consumption of a system as a target, under the condition that the conventional load and the wind power prediction are known, optimizing source load scheduling by limiting constraint conditions such as upper and lower limit constraint, climbing and the like of high-energy-consumption load of an electric arc furnace;
further, the step S2 of establishing a second optimizing model that minimizes the total cost of the system operation as an objective function based on the optimizing wind power consumption model in the step S1 includes:
the result of the minimum air quantity model is applied to the cost optimization function, so that the purpose of accurate optimization is achieved;
further, the step S3 of selecting a heuristic algorithm to solve the source-load coordination scheduling optimization model includes:
by solving the equality constraint, the inequality constraint and the objective function, an optimal scheduling scheme is found to optimize the efficiency and economy of the system operation.
The beneficial effects of the invention are as follows:
the source-load coordination scheduling method considering the fine modeling of the electric arc furnace load is mainly oriented to the problems of uncertainty of energy supply, shortage of non-renewable resources and the like, provides a new optimization method for the demand side response of the electric power system, and further improves the operation efficiency and the economy of the electric power system. After the high energy consumption load adjustment of adding the fused magnesium is adopted, the wind power consumption is greatly increased, the output of the thermal power unit is smooth, and the peak regulation capacity of the thermal power unit is equivalent to being increased. And through the double optimization method, the total running cost of the system can be reduced under the guarantee of improving the wind power utilization rate.
Drawings
FIG. 1 is a flow chart of a source-load dual optimization model in the invention;
fig. 2 is a schematic diagram of a system structure with high energy consumption load participating in scheduling in the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention relates to a source load coordination scheduling method considering fine modeling of electric arc furnace load, which is implemented according to the following steps:
step S1: establishing a first re-optimization model with maximum wind power consumption as an objective function after the high energy consumption load of fused magnesium is used to participate in a source load joint regulation mode;
step S2: based on the optimized wind power consumption model in the step S1, a second optimizing model which minimizes the total cost of system operation as an objective function is established;
step S3: a heuristic algorithm is selected to solve the source-load coordination scheduling optimization model;
according to the power system source-load coordination optimization scheduling method considering fine modeling regulation of the high-energy-carrying load of the electric arc furnace, which is provided by the invention, after the high-energy-consumption load of the electric smelting magnesium is used as the load side of the power system to be regulated, the wind power consumption is greatly increased, the output of the thermal power unit is smooth, and the peak regulation capacity of the thermal power unit is equivalent to being increased. And through the double optimization method, the total running cost of the system can be reduced under the guarantee of improving the wind power utilization rate.
Specifically, the step S1 includes:
establishing a first re-optimization model with maximum wind power consumption as an objective function after the high energy consumption load of fused magnesium is used to participate in a source load joint regulation mode;
on the basis of meeting various constraint conditions such as thermal power generating units, wind power, loads and the like, the day-ahead optimization method aims at the maximum wind power consumption of the system, and under the condition that the conventional loads and the wind power prediction are known, the source load scheduling is optimized by limiting constraint conditions such as upper and lower limit constraint, climbing and the like of high-energy-consumption loads of the electric arc furnace. The objective function of the day-ahead optimization method is as follows:
wherein: t is the time period number of the scheduling period; n (N) wind The number of wind farms is the number; p (P) t W-act，i And (3) the output force of the wind farm i in the period T is shown, and DeltaT is the number of the wind abandoning period.
The constraint conditions comprise an arc furnace high-load energy load adjusting power climbing constraint, an upper limit constraint, a lower limit constraint and a same-period energy balance constraint. Thermal power generating unit operation constraint, wind power output constraint, system power balance constraint and the like.
(1) System constraints.
(1) A power balance constraint.
Wherein: p (P) t Ther The total active output of the thermal power generating unit in the period t is obtained; p (P) t Load-fore Active predicted power for a system regular load during a period t; n (N) Mg For the number of electric arc furnaces, P t Mg，k Active power of single electric arc furnace in t period, P t H，k The power is regulated for a single electric arc furnace during period t.
(2) And rotating the standby constraint.
Because wind power generation has randomness, a certain error exists in wind power prediction power, and in order to avoid that wind power output prediction errors and conventional load prediction errors influence the optimal operation of a system, positive and negative rotation spare capacities are increased to cope with the system prediction errors.
Wherein: r is R t L,+ And R is t L,- Positive and negative rotations required for coping with load prediction errors in t time periods are respectively reserved; r is R t W,+ And R is t W,- Positive and negative rotations required for coping with wind power prediction errors in t time periods are respectively reserved.
(2) Wind power output constraint conditions.
Wherein: p (P) t W-pre，i The output is predicted for the active power of wind farm i in period t.
(3) Thermal power generating unit operation constraint conditions.
(1) And (5) upper and lower limits of output power are constrained.
(2) And (5) restraining the climbing speed of the thermal power generating unit.
Wherein: p (P) Ther t-1 The total active output of the conventional unit in the t-1 period is obtained; p (P) Ther up And P Ther down The ascending climbing speed and the descending climbing speed of the thermal power generating unit are respectively.
(4) The high energy load of the arc furnace adjusts the power constraint condition.
(1) The power upper and lower limit constraints are adjusted.
Wherein: p (P) Hmax And P Hmin And respectively adjusting the upper and lower power limits for the high-energy load of the electric arc furnace.
(2) And (5) the climbing speed constraint of the arc furnace.
Wherein: p (P) H t For the total active output of the conventional unit in the period t, P H t-1 The total active output of the conventional unit in the t-1 period is obtained; p (P) H,up And P H,down The ascending climbing speed and the descending climbing speed of the thermal power generating unit are respectively.
(3) Adjusting energy balance constraints
T Mg The energy from the full start to the stop of each electric arc furnace is kept unchanged (irrespective of the heat dissipation of the furnace body itself).
Specifically, the step S2 includes:
based on the optimized wind power consumption model in the step S1, a second optimizing model which minimizes the total cost of system operation as an objective function is established;
the objective function of the second optimization is to minimize the total cost of system operation, i.e., the sum of the operation cost of the thermal power plant and the penalty cost of the system wind curtailment. The overall cost of the system operation is then optimized. The mathematical description is as follows:
minF＝M G +M W (13)
wherein: f is the total cost, M G The operation cost of the thermal power generating unit is; m is M W Punishment of costs for wind curtailment; u (U) t Gj Is the start-stop state variable of the thermal power unit j in the period t, U t Gj =0 indicates that the unit is in a stop state in the period t, U t Gj =1 indicates that the unit is in an on state during the period t; a, a j 、b j 、c j Is an operation cost parameter of the thermal power generating unit j. η is the unit wind abandon punishment cost of the wind farm i in the system; deltat is the number of wind curtailed periods of the system,predicting the force for wind power->Wind power consumption of system, N W And N G The number of wind power units and the number of thermal power units are respectively represented.
The constraint conditions comprise output power balance constraint of the thermal power unit, climbing speed constraint of the thermal power unit and output power upper and lower limit constraint of the thermal power unit.
(1) And (3) power balance constraint of the thermal power generating unit.
Wherein: n (N) G The unit number is the unit number of the thermal power unit; p (P) t Gj For thermal power generating unit j in t periodIs a combination of the force of the spring.
(2) And (5) the climbing speed constraint of the thermal power generating unit.
Wherein: p (P) Gj t-1 The output of the thermal power unit j in the t-1 period is given; p (P) Gj,up And P Gj,down The ascending climbing output limit and the descending climbing output limit of the thermal power generating unit j are respectively.
(3) And the upper limit and the lower limit of the output power of the thermal power generating unit are constrained.
Wherein: p (P) Gjmax And P Gjmin The upper and lower limits of the output power of the thermal power generating unit j are respectively set.
Specifically, in the step S3,
and (3) selecting a heuristic algorithm to solve a source-load coordination scheduling optimization model, wherein an adopted objective function is as follows:
wherein, G function is used for showing the goal of minimizing the air-discarding quantity, O function is used for realizing the minimum daily air-discarding and coal consumption cost. The variable x to be optimized comprises thermal power unit output, wind power planning power, electric arc furnace high-load energy load adjusting power, running cost of the thermal power unit and wind discarding punishment cost of the system. Meanwhile, the start-stop state y of the thermal power generation unit and the wind turbine unit is also an important decision variable. Our optimization problem is limited by a series of equality constraints, including system power balance constraints and thermal power plant power balance constraints. In addition, inequality constraints such as wind power output constraint, thermal power unit operation constraint, and limitation of high-energy load regulation power of the arc furnace are also considered. By addressing these constraints and objective functions, we aim to find an optimal scheduling scheme to optimize the efficiency and economy of system operation.
Examples
The research adopts a new day-ahead source-load coordination optimization method to comprehensively optimize wind power output, thermal power unit output, high-load energy load regulation power of the electric arc furnace and system operation cost. Meanwhile, the obtained optimal scheme is compared with a method which does not consider the high-load energy load adjustment of the electric arc furnace. For the model that does not consider the electric arc furnace high energy load regulated power, the regulated power is set to 0, and the other conditions are the same as the model proposed in the present study. The model which does not consider the high-load energy load adjustment of the electric arc furnace is called a scheme I, and the double optimization method which is proposed in the research and is used for considering the high-load energy load adjustment of the electric arc furnace is called a scheme II. The difference in performance of the two schemes was evaluated by comparing the results of the two schemes.
In the scheme I, the high energy consumption load is a constant value, the total output of the thermal power generating unit reaches the lowest level (near 0:00 and 6:00) in 4 specific time periods, and the output reaches the highest level (near 11:00 and 17:00) in 5 specific time periods. In the second scheme, the value of the high energy consumption load is scheduling adjustment, and the power output of the thermal power generating unit reaches the lowest level (near 0:00) in 1 specific time period, and reaches the highest level (near 11:00 and 17:00) in 4 specific time periods. The peak value and the valley value of the thermal power generating unit are obviously reduced before and after the high energy consumption load participates in regulation. When the wind speed is high, the wind power consumption is increased by improving the high energy consumption load demand of the fused magnesium, so that the wind power electricity limiting or wind discarding condition is avoided; when the wind speed is low, the power supply pressure is reduced by reducing the power demand, the dependence on the thermal power unit is reduced, and the peak regulation capacity of the thermal power unit is increased.
Under the condition that the high energy consumption of the fused magnesium does not participate in regulation in the scheme one, the total output of the thermal power generating unit is uneven, and the total output is higher in the time period of 9:00-12:00 and the time period of 17:00-23:00 compared with other time periods, and the average output is 800MW. While the valley period output is 550MW. The total period output average was 700MW. The fluctuation ratio was calculated to be about 0.3571. In the second scheme, the output of each thermal power unit is obviously even and smoother than that of the first scheme. The output average value of the peak period is 770MW, the output average value of the valley period is 620MW, and the output of the total period is unchanged. The fluctuation ratio was calculated to be 0.2143. Compared with the scheme I, the scheme II has the advantage that the fluctuation degree of the thermal power generating unit is obviously reduced. The adjusting capacity of the thermal power generating unit is increased.
In two different schemes, the running cost of the thermal power generating unit is slightly different. However, through the second embodiment, the regulation capability of the system is improved, the wind power receiving amount is greatly increased, and the cost of the system for discarding wind is greatly reduced. Thus, the overall operating costs of the system are lower in case two compared to case one, and this also brings about a significant economic benefit, since peak regulation and the limited wind power is consumed by the high load capacity of the arc furnace.
The source-load coordination scheduling method considering the fine modeling of the electric arc furnace load is mainly oriented to the problems of uncertainty of energy supply, shortage of non-renewable resources and the like, provides a new optimization method for the demand side response of the electric power system, and further improves the operation efficiency and the economy of the electric power system. After the high energy consumption load adjustment of adding the fused magnesium is adopted, the wind power consumption is greatly increased, the output of the thermal power unit is smooth, and the peak regulation capacity of the thermal power unit is equivalent to being increased. And through the double optimization method, the total running cost of the system can be reduced under the guarantee of improving the wind power utilization rate.
Claims (3)
1. A source load coordination scheduling method considering fine modeling of electric arc furnace load is characterized by comprising the following steps:
step S1: establishing a first re-optimization model with maximum wind power consumption as an objective function after the high energy consumption load of fused magnesium is used to participate in a source load joint regulation mode;
step S2: based on the first re-optimization model in the step S1, a second re-optimization model which minimizes the total cost of system operation as an objective function is established;
step S3: a heuristic algorithm is selected to solve the source-load coordination scheduling optimization model;
the step S1 includes:
on the basis of meeting various constraint conditions of a thermal power generating unit, wind power and load, optimizing source load scheduling by limiting upper and lower limit constraint and climbing constraint conditions of high-energy-consumption load of an electric arc furnace under the condition that conventional load and wind power prediction are known by taking the maximum wind power consumption of a system as a target; the objective function of the day-ahead optimization method is as follows:
wherein T is the time period number of the scheduling period; n (N) wind The number of wind farms is the number; p (P) t W-act，i For the output force of the wind farm i in the T period, deltaT is the number of the wind abandoning period;
the step S2 includes:
the objective function of the second optimization is to minimize the total cost of the system operation, namely the sum of the operation cost of the thermal power generating unit and the punishment cost of the system waste wind; then, optimizing the total cost of the system operation; the mathematical description is as follows:
minF＝M G +M W
wherein F is the total cost, M G The operation cost of the thermal power generating unit is; m is M W To punish the cost for wind abandon, U t Gj Is the start-stop state variable of the thermal power unit j in the period t, U t Gj =0 indicates that the unit is in a stop state in the period t, U t Gj =1 indicates that the unit is in an on state during the period t; a, a j 、b j 、c j The method is characterized in that the method is an operation cost parameter of a thermal power unit j, wherein eta is a unit wind abandon punishment cost of a wind power plant i in the system; delta T is the number of wind abandoning periods of the system,predicting the force for wind power->Wind power consumption of system, N W And N G Respectively representing the number of wind power units and the number of thermal power units;
the step S3 includes:
the objective function used is as follows:
wherein, the G function is used for representing the target of minimizing the air discarding quantity, and the O function is used for realizing the lowest daily air discarding and coal consumption cost; the variable x to be optimized comprises thermal power unit output, wind power planning power, electric arc furnace high-load energy load adjusting power, running cost of the thermal power unit and wind discarding punishment cost of the system; the start-stop state y of the thermal power generation and wind turbine generator is also an important decision variable.
2. The source load coordination scheduling method considering the fine modeling of the electric arc furnace load according to claim 1, wherein the constraint conditions in the step S1 comprise an electric arc furnace high-load energy load adjustment power climbing constraint, an upper limit constraint, a lower limit constraint, a same-cycle energy balance constraint, a thermal power unit operation constraint, a wind power output constraint and a system power balance constraint;
(1) System constraints
(1) Power balance constraint
Wherein: p (P) t Ther Is a thermal power machineTotal active force of the group in period t; p (P) t Load-fore Active predicted power for a system regular load during a period t; n (N) Mg For the number of electric arc furnaces, P t Mg，k Active power of single electric arc furnace in t period, P t H，k Regulating power of a single electric arc furnace in a period t;
(2) rotation reserve constraint
Because wind power generation has randomness, a certain error exists in wind power prediction power, and in order to avoid that wind power output prediction errors and conventional load prediction errors influence the optimal operation of a system, positive and negative rotation spare capacities are increased to cope with the system prediction errors;
wherein: r is R t L,+ And R is t L,- Positive and negative rotations required for coping with load prediction errors in t time periods are respectively reserved; r is R t W,+ And R is t W,- Positive and negative rotations required by the wind power prediction error of the t period are respectively used for standby;
(2) Wind power output constraint condition
Wherein: p (P) t W-pre，i Active predicted output of the wind farm i in a t period;
(3) Thermal power generating unit operation constraint condition
(1) Upper and lower limit constraints for output power
(2) Climbing speed constraint of thermal power generating unit
Wherein: p (P) Ther t-1 The total active output of the conventional unit in the t-1 period is obtained; p (P) Therup And P Therdown The ascending climbing speed and the descending climbing speed of the thermal power generating unit are respectively.
3. The source load coordination scheduling method considering fine modeling of electric arc furnace load according to claim 2, wherein the electric arc furnace high-load energy load adjustment power constraint condition is:
(1) adjusting power upper and lower limit constraints
Wherein: p (P) Hmax And P Hmin Respectively regulating the upper and lower limits of power for the high-energy load of the electric arc furnace;
(2) climbing speed constraint of electric arc furnace
Wherein: p (P) H t For the total active output of the conventional unit in the period t, P H t-1 The total active output of the conventional unit in the t-1 period is obtained; p (P) H,up And P H,down The ascending climbing speed and the descending climbing speed of the thermal power generating unit are respectively;
(3) adjusting energy balance constraints
T Mg For the number of time periods of one cycle of the electric arc furnace operation, the energy from the full load start to the stop of each electric arc furnace is kept unchanged, and the heat dissipation of the furnace body in the middle is not considered.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5621654A (en) * | 1994-04-15 | 1997-04-15 | Long Island Lighting Company | System and method for economic dispatching of electrical power |
| CN105447599A (en) * | 2015-11-30 | 2016-03-30 | 东北电力大学 | Heat-storage heat-power co-generation unit and electric boiler based wind curtailment absorption coordinated dispatching model |
| CN109284943A (en) * | 2018-11-17 | 2019-01-29 | 国网辽宁省电力有限公司阜新供电公司 | A kind of electric pole type electric boiler configuration method improving wind electricity digestion capability |
| CN112182915A (en) * | 2020-10-30 | 2021-01-05 | 东北电力大学 | Optimized scheduling method and system for cooperatively promoting wind power consumption |
| CN112615370A (en) * | 2020-12-17 | 2021-04-06 | 国网辽宁省电力有限公司鞍山供电公司 | Wind power consumption coordination control method based on electric smelting magnesium load |
-
2023
- 2023-09-22 CN CN202311234704.0A patent/CN117293801B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5621654A (en) * | 1994-04-15 | 1997-04-15 | Long Island Lighting Company | System and method for economic dispatching of electrical power |
| CN105447599A (en) * | 2015-11-30 | 2016-03-30 | 东北电力大学 | Heat-storage heat-power co-generation unit and electric boiler based wind curtailment absorption coordinated dispatching model |
| CN109284943A (en) * | 2018-11-17 | 2019-01-29 | 国网辽宁省电力有限公司阜新供电公司 | A kind of electric pole type electric boiler configuration method improving wind electricity digestion capability |
| CN112182915A (en) * | 2020-10-30 | 2021-01-05 | 东北电力大学 | Optimized scheduling method and system for cooperatively promoting wind power consumption |
| CN112615370A (en) * | 2020-12-17 | 2021-04-06 | 国网辽宁省电力有限公司鞍山供电公司 | Wind power consumption coordination control method based on electric smelting magnesium load |
Non-Patent Citations (1)
| Title |
|---|
| 考虑储能***调度与风电消纳的峰谷分时电价优化模型研究;李艳梅等;《电网技术》;20221130;第46卷(第11期);4141-4149 * |
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