CN112508401A - Thermal power generating unit deep peak regulation economic dispatching method under large-scale new energy grid-connected condition - Google Patents

Thermal power generating unit deep peak regulation economic dispatching method under large-scale new energy grid-connected condition Download PDF

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CN112508401A
CN112508401A CN202011413796.5A CN202011413796A CN112508401A CN 112508401 A CN112508401 A CN 112508401A CN 202011413796 A CN202011413796 A CN 202011413796A CN 112508401 A CN112508401 A CN 112508401A
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thermal power
generating unit
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于国强
朱志莹
刘克天
胡尊民
张天海
刘娜娜
杨小龙
肖新宇
史毅越
汤可怡
高爱民
殳建军
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Jiangsu Fangtian Power Technology Co Ltd
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Abstract

A thermal power generating unit deep peak regulation economic dispatching method under the condition of large-scale new energy grid connection is characterized in that thermal power generating unit deep peak regulation cost models at different peak regulation stages are established according to the thermal power generating unit deep peak regulation operation characteristics; the volatility and the randomness of wind power and photovoltaic output are considered, the reserve capacity cost is introduced to deal with prediction errors and emergencies, a deep peak shaving economic dispatching model considering oil consumption, the service life of a unit and the environment is established, and then a final dispatching scheme is obtained by solving the economic dispatching model. According to the method, the economy of the thermal power generating unit in deep peak regulation under large-scale new energy grid connection is analyzed from the aspects of peak regulation depth, new energy consumption, thermal power enterprise income and the like, and reference can be provided for the thermal power generating unit in the multi-energy power system in deep peak regulation.

Description

Thermal power generating unit deep peak regulation economic dispatching method under large-scale new energy grid-connected condition
Technical Field
The invention relates to the field of new energy power generation, in particular to a thermal power generating unit deep peak shaving economic dispatching method under the condition of large-scale new energy grid connection.
Background
In recent years, China has a good effect on the aspect of energy structure transformation, the installed capacity of newly increased wind power is continuously improved, solar power generation keeps steadily increasing, and the accumulated capacity of distributed photovoltaic power generation breaks through 6000 million kW. In the future, new energy still is the fastest growing power type, large-scale new energy is connected to the power grid, the load peak-valley difference of the system is increased day by day, and the demand of system peak regulation is increased more and more.
At present, a thermal power generating unit is a main power supply for system peak regulation, and with large-scale new energy grid connection, increased peak regulation tasks are also borne by the thermal power generating unit. With the large-scale increase of installed capacities of wind power and photovoltaic, the new energy grid-connected consumption is increased year by year, and the unit needs to be subjected to deep peak regulation transformation so as to operate at 30% -50% of rated power. How to balance the deep peak regulation and the new energy consumption is particularly necessary for researching the economy of the wind power, photovoltaic and thermal power generating units under the deep peak regulation.
The conventional peak regulation depth is further expanded by deep peak regulation, the output of a thermal power generating unit is reduced, the consumption of new energy can be improved, and the peak regulation pressure of a system is relieved. In the economic dispatching research under the large-scale new energy grid connection, only a conventional peak regulation stage is usually considered, and the economic analysis on a deep peak regulation stage is less. At present, an economic dispatching model of a conventional peak shaving unit is no longer suitable for a large-scale new energy grid-connected current situation power grid. After the thermal power generating unit participates in deep peak shaving, the economic calculation of the thermal power generating unit becomes more complex, and not only the cost change of the thermal power generating unit is considered, but also the environmental benefit is considered.
Currently, economic analysis of peak shaver is mainly focused on conventional peak shaver. In the aspect of economic analysis and research of deep peak shaving, wind power is mainly used as a background or is only connected with photovoltaic, and the research on multi-energy complementary combined dispatching under the deep peak shaving of wind power, photovoltaic and thermal power units is less, and new energy consumption and environmental benefits brought by the new energy consumption are often ignored.
Disclosure of Invention
The deep peak regulation economic dispatching method is researched by aiming at a thermal power generating unit deep peak regulation model under large-scale new energy grid connection, comprehensive cost is taken as a target function, reserve capacity is introduced to deal with prediction errors of wind power, photovoltaic and loads and some emergencies, and the deep peak regulation economic dispatching method considering oil consumption, unit service life and environment is obtained.
A thermal power generating unit deep peak regulation economic dispatching method under the condition of large-scale new energy grid connection comprises the following steps:
step1, dividing the deep peak shaving of the thermal power generating unit into 2 stages of oil-throwing-free deep peak shaving DPR and oil-throwing deep peak shaving DPRO, and calculating the energy consumption cost of the deep peak shaving operation, wherein the energy consumption cost comprises the coal consumption cost of the thermal power generating unit, the service life loss cost of the unit, the oil-throwing cost and the environmental additional cost, so as to obtain a fixed cost function of the deep peak shaving process of the thermal power generating unit;
step2, calculating deep peak-shaving operation compensation benefits according to compensated peak-shaving service compensation rules of different areas;
step3, introducing the rotating standby cost, and obtaining the rotating standby cost according to the load, the prediction error rates of the wind power and the photovoltaic and the power of the load, the wind power and the photovoltaic at each moment;
step4, according to the analysis of the fixed cost, the operation compensation income and the rotation standby cost in the deep peak shaving process of the thermal power generating unit in the step, obtaining an objective function with the minimum comprehensive operation cost and relevant constraints;
and 5, solving the objective function to obtain a final scheduling scheme.
Further, in step1, the various cost calculations are specifically as follows:
the coal consumption cost is calculated by adopting the consumption characteristic, and the running coal consumption cost is as follows:
Figure BDA0002819453030000031
in the formula, CcThe coal consumption cost of the thermal power generating unit is reduced; pi,tGenerating capacity of the thermal power generating unit; a, b and c are coal consumption characteristic coefficients; scoalIs the coal price;
calculating the unit life loss cost according to the low cycle fatigue characteristic relation of the rotor material, and obtaining the unit life loss cost as follows according to a Manson-coffee formula:
Figure BDA0002819453030000032
in the formula, A is the service life loss cost of the unit; delta is the actual operation loss coefficient, and the loss coefficient of oil injection is larger than that of deep peak shaving without oil injection; suCost for purchasing machines; n is a radical off(Pi,t) Representing the cycle number of the rotor fracturing;
in the DPRO stage, the oil input and consumption cost is as follows:
B(Pi,t)＝Ocost×So (3)
in the formula, B is the oil feeding cost of the unit; o iscostThe fuel consumption is calculated; soIs the oil price;
in the oil feeding depth peak regulation stage, the environment additional cost of the oil feeding depth peak regulation stage is as follows:
Ev＝Ocost×Wp+Sp(Pi,t,Ocost) (4)
in the formula, EvAdding cost to the environment; wpThe unit fuel oil pollution discharge cost; spAnd the pollution discharge penalty function represents the penalty of the pollution discharge exceeding the standard.
Further, in step1, based on various costs, the fixed cost function of the thermal power generating unit in the deep peak shaving process is as follows:
Figure BDA0002819453030000033
the starting and stopping cost of the unit in a period of time is as follows:
Figure BDA0002819453030000041
the deep peak regulation cost considering the start-stop cost of the unit is as follows:
Figure BDA0002819453030000042
in the formula (f)costThe unit deep peak shaving cost; csThe unit start-stop cost; paThe maximum output of the unit without oil injection depth peak regulation; pbThe maximum output of peak shaving for the oil feeding depth of the unit; pmax、PminRespectively the maximum output and the minimum output of the thermal power generating unit; t is the number of scheduling time segments; n is the number of units; u shapei,tFor the start-stop state and operation of the unit Ui,t1, U at shutdowni,t＝0；Si,upFor the cost of start-up, Si,downFor the cost of down time.
Further, in step2, according to the actual scheduling plan, calculating the compensation yield of the deep peak shaving of the thermal power generating unit by taking every 15min as a unit statistical cycle, wherein the compensation yield is calculated as follows:
Figure BDA0002819453030000043
in the formula, CRevTo compensate for the gain; t is the number of compensated peak shaving hours in the j gear; c. CpjCompensating the price for the peak load regulation for the j-th gear; pdejThe value of the peak shaving capacity of the depth of the j gear is the total peak shaving capacity minus the conventional peak shaving capacity.
Further, in step3, a rotational standby cost is introduced under the deep peak shaving operation, and the rotational standby cost is as follows:
Figure BDA0002819453030000051
in the formula, CbySpare cost for spinning; k is a radical ofbA system standby cost factor; l, F, G is the prediction error rate of load, wind power and photovoltaic; plt、Pft、PgtThe t moment load, the wind power and the photovoltaic power are respectively.
Further, in step4, the objective function with the minimum comprehensive operation cost is established as follows:
minC＝Cs+Cby-CRev (9)
the constraint of the objective function is as follows:
output restraint of the thermal power generating unit:
Pmin≤Pi,t≤Pmax (10)
neglecting the system power balance constraint of network loss:
Figure BDA0002819453030000052
unit climbing restraint:
Ui,t-1Pi,t-1-αdowni≤pi,t≤αupi+Ui,t-1Pi,t-1 (12)
and (3) limiting the start-stop time:
Figure BDA0002819453030000053
and (3) new energy output constraint:
Figure BDA0002819453030000054
and (3) system rotation standby constraint:
Figure BDA0002819453030000061
in the formula: alpha is alphadowni、αupiRespectively limiting the downward climbing rate and the upward climbing rate of the unit i;
Figure BDA0002819453030000062
Figure BDA0002819453030000063
respectively keeping the unit i running and stopping for a time t; t isoni、ToffiRespectively the minimum continuous running time and the minimum shutdown time of the unit i;
Figure BDA0002819453030000064
respectively predicting values of wind power output and photovoltaic output;
Figure BDA0002819453030000065
respectively the maximum output and the minimum output of the unit i in the time period t; pptThe prediction error of the system is calculated by the following formula:
Ppt＝LPlt+FPft+GPgt (16)
further, in step5, a branch-and-bound method is adopted to seek a global optimal solution, and the flow is as follows:
step 5-1, setting the value f of the optimal solution to be + ∞, and solving a corresponding relaxation problem; judging whether the original relaxation problem is feasible or not, and if an optimal solution meeting the requirements is found, determining the optimal solution of the original problem; otherwise, branching the original problem to seek the optimal solution;
step 5-2, branching; selecting one solution which does not conform to the integer constraint condition from the optimal solutions, and setting the value of the solution as BjWith BjAnd Bj+1 is an upper bound, and two constraint conditions are respectively constructed to form two subproblems;
step 5-3, delimiting; judging whether the sub-problem has an integer solution, if so, finding out a target value of the original relaxation problem and setting the target value as a lower bound of the branch problem;
step 5-4, sequentially selecting the relaxin problems from the queue to be branched to carry out branch solution, and correcting the upper and lower boundaries of the original problem;
step 5-5, according to the pruning principle, cutting off part of the invalid sub-problems;
and 5-6, checking the solutions and targets of all the branches.
The invention achieves the following beneficial effects: based on the operating state and characteristics of the thermal power generating unit under deep peak regulation, the method establishes cost models of the thermal power generating unit at different peak regulation stages, introduces the spare capacity cost to overcome the fluctuation of output prediction of new energy and extra cost brought by some emergencies, researches the economic dispatching problem of deep peak regulation of the thermal power generating unit under wind power and photovoltaic grid connection, provides a dispatching method for enterprises, can analyze the economy of deep peak regulation of the thermal power generating unit under large-scale new energy grid connection, and can provide reference for deep peak regulation of the thermal power generating unit of a multi-energy power system.
Drawings
Fig. 1 is a flowchart illustrating steps of a scheduling method according to an embodiment of the present invention.
FIG. 2 is a table of the depth peak-shaving compensation criteria of the east China area in the embodiment of the present invention.
FIG. 3 is a graph illustrating an exemplary daily load prediction in an embodiment of the present invention.
FIG. 4 is a wind power and photovoltaic output prediction curve diagram in the embodiment of the invention.
Fig. 5 is an economic dispatch index table under different peak shaver depths in the embodiment of the present invention.
Fig. 6 is a diagram illustrating load prediction power and a thermal power generating unit according to an embodiment of the present invention.
Fig. 7 is a diagram of wind power prediction power and grid-connected absorption power in the embodiment of the invention.
Fig. 8 is a diagram of photovoltaic predicted power and grid-connected absorbed power in an embodiment of the present invention.
Fig. 9 is a diagram of the operation of the thermal power generating unit under the peak shaving depth of 55% in the embodiment of the invention.
Fig. 10 is a diagram of the operation of the thermal power generating unit under the condition of 60% peak shaving depth in the embodiment of the invention.
Fig. 11 is a diagram of the operation of the thermal power generating unit under the 65% peak shaving depth in the embodiment of the invention.
Fig. 12 is a daily income table of a thermal power enterprise in the embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the drawings in the specification.
According to the operating characteristics and the energy consumption characteristics of the thermal power generating unit, the deep peak shaving of the thermal power generating unit can be divided into 2 stages of no-oil-feeding deep peak shaving (DPR) and oil-feeding deep peak shaving (DPRO).
Different from the conventional peak regulation, the operation cost of the thermal power generating unit in the deep peak regulation stage is greatly changed. The deep peak regulation not only contains explicit cost of fuel and the like, but also includes implicit cost of oil feeding cost, abrasion life loss, environmental pollution and the like caused by insufficient combustion for increasing the stable combustion capacity of the boiler, so that the economic benefit of the thermal power plant is greatly reduced.
(1) Coal consumption cost of thermal power generating unit
Coal consumption costs are typically calculated using consumption characteristics. The coal consumption cost for operation is as follows:
Figure BDA0002819453030000081
in the formula: ccThe coal consumption cost of the thermal power generating unit is reduced; pi,tGenerating capacity of the thermal power generating unit; a, b and c are coal consumption characteristic coefficients; scoalIs the coal price.
(2) Loss cost of unit life
And calculating the service life loss cost of the unit according to the low-cycle fatigue characteristic relation of the rotor material. Referring to the Manson-coffee formula, the available unit life loss cost is as follows:
Figure BDA0002819453030000082
in the formula: a is the unit life loss cost; delta is the actual operation loss coefficient, and the loss coefficient of oil injection is larger than that of deep peak shaving without oil injection; suTo purchase the machine cost, Nf(Pi,t) Representing the cycle of rotor fracturing.
(3) Cost of oil injection
In the DPRO stage, the combustion stability of the boiler is reduced, and oil is required to be added for combustion supporting so as to ensure the normal operation of the unit. The oil feeding and consumption cost is as follows:
B(Pi,t)＝Ocost×So (3)
in the formula: b is the oil feeding cost of the unit; o iscostFor fuel consumption, SoIs the oil price.
(4) Environmental additional cost
In the deep peak regulation stage of oil feeding, pollutants such as smoke dust, nitrogen oxides and the like can be generated after oil feeding and combustion, so that the waste pollution discharge of a thermal power plant is increased, and government fines are caused by excessive pollutant discharge. Therefore, the additional cost of the environment in the oil feeding depth peak regulation stage is as follows:
Ev＝Ocost×Wp+Sp(Pi,t,Ocost) (4)
in the formula: evAdding cost to the environment; wpThe unit fuel oil pollution discharge cost; spAnd the pollution discharge penalty function represents the penalty of the pollution discharge exceeding the standard.
Based on the cost, the fixed cost function of the thermal power generating unit in the deep peak regulation process is as follows:
Figure BDA0002819453030000091
the starting and stopping cost of the unit in a period of time is as follows:
Figure BDA0002819453030000092
the deep peak regulation cost considering the start-stop cost of the unit is as follows:
Figure BDA0002819453030000093
in the formula: f. ofcostThe unit deep peak shaving cost; csThe unit start-stop cost; paThe maximum output of the unit without oil injection depth peak regulation; pbThe maximum output of peak shaving for the oil feeding depth of the unit; pmax、PminRespectively the maximum output and the minimum output of the thermal power generating unit; t is the number of scheduling time segments; n is the number of units; u shapei,tFor the start-stop state and operation of the unit Ui,t1, U at shutdowni,t＝0；Si,upFor the cost of start-up, Si,downFor the cost of down time.
And the depth peak regulation compensation is determined according to the actual power generation output of the unit. Taking the compensated regulation rule of the peak regulation service in the east China area as an example, the reference of the peak regulation service is 60% of the load rate of the unit, and the conventional coal-fired unit compensates the low power generation amount below the basic peak regulation lower limit. The compensation criteria is in gear 4 as shown in figure 2.
And calculating the compensation yield of the deep peak shaving of the thermal power generating unit by taking every 15min as a unit statistical period according to the actual scheduling plan. The compensation yield is calculated as follows:
Figure BDA0002819453030000101
in the formula: cRevTo compensate for the gain; t is the number of compensated peak shaving hours in the j gear; c. CpjCompensating the price for the peak load regulation for the j-th gear; pdejThe value of the peak shaving capacity of the depth of the j gear is the total peak shaving capacity minus the conventional peak shaving capacity.
The fluctuation and randomness of the new energy output increases the uncertainty of the operation of the power grid, so that extra spare capacity needs to be added to deal with prediction errors and emergencies. The invention introduces the rotary standby cost under the deep peak regulation operation, and the rotary standby cost is as follows:
Figure BDA0002819453030000102
in the formula: cbySpare cost for spinning; k is a radical ofbA system standby cost factor; l, F, G is the prediction error rate of load, wind power and photovoltaic; plt、Pft、PgtThe t moment load, the wind power and the photovoltaic power are respectively.
According to the analysis of the deep peak regulation cost of the thermal power generating unit, establishing an objective function with the minimum comprehensive operation cost as follows:
minC＝Cs+Cby-CRev (9)
the constraint of the objective function of equation (9) is as follows:
(a) thermal power unit output constraint
Pmin≤Pi,t≤Pmax (10)
(b) System power balance constraint (neglecting network loss)
Figure BDA0002819453030000111
(c) Unit climbing restraint
Ui,t-1Pi,t-1-αdowni≤pi,t≤αupi+Ui,t-1Pi,t-1 (12)
(d) Start-stop time constraint
Figure BDA0002819453030000112
(e) New energy output constraint
Figure BDA0002819453030000113
(f) System rotational back-up constraint
Figure BDA0002819453030000114
In the formula: alpha is alphadowni、αupiRespectively limiting the downward climbing rate and the upward climbing rate of the unit i;
Figure BDA0002819453030000115
Figure BDA0002819453030000116
respectively keeping the unit i running and stopping for a time t; t isoni、ToffiRespectively the minimum continuous running time and the minimum shutdown time of the unit i;
Figure BDA0002819453030000117
respectively predicting values of wind power output and photovoltaic output;
Figure BDA0002819453030000118
respectively the maximum output and the minimum output of the unit i in the time period t; pptIs the prediction error of the system, and the calculation formula is
Ppt＝LPlt+FPft+GPgt (16)
The invention adopts Branch and Bound method (Branch and Bound) to seek global optimum solution, the method has good convergence for processing pure integer program and mixed integer program, the essence is based on 'relaxation', 'branching', 'delimiting' and 'pruning', and the final answer is found by repeating iteration for different Branch variables and subproblems, the flow is as follows:
step 1: setting the value f of the optimal solution to be + ∞, and solving the corresponding relaxation problem; judging whether the original relaxation problem is feasible or not, and if an optimal solution meeting the requirements is found, determining the optimal solution of the original problem; otherwise, the original problem is branched to seek the optimal solution.
Step 2: and (4) branching. Selecting one solution which does not conform to the integer constraint condition from the optimal solutions, and setting the value of the solution as BjWith BjAnd BjThe +1 is an upper bound, and two constraint conditions are respectively constructed to form two subproblems.
Step 3: and (4) delimiting. And judging whether the sub-problem has an integer solution or not, if so, finding out the target value of the original relaxation problem and setting the target value as the lower bound of the branch problem.
Step 4: and sequentially selecting the relaxation subproblems from the queue to be branched to carry out branch solution, and correcting the upper and lower boundaries of the original problem.
Step 5: according to the pruning principle, partial nullification problems are pruned.
Step 6: the solutions and targets of all branches are examined.
The present embodiment is simulated by a typical 10-machine system. Wind power and photovoltaic power generation respectively replace 2 thermal power generating units to form a simulation system, wherein the installed capacity of thermal power is 5000MW (2 units each of 1000MW, 800MW, 500MW and 300 MW), and the installed capacity of new energy is 2000MW (1200 MW and 800MW of photovoltaic installed power). A typical daily load prediction curve is shown in fig. 3.
Referring to the peak regulation standard of thermal power generating units in east China, when the load rate of the units is lower than 60%, deep peak regulation operation is performed, when the load rate of the units is lower than 45%, deep peak regulation operation of oil injection is performed, and the minimum technical output of all the units is set to be 30% of the rated capacity of the units. The coal price is 545 yuan/ton, the oil consumption cost is 6092 yuan/ton, and the environmental additional cost is 408 yuan/ton. The load prediction error rate L is 12%, the wind power and photovoltaic prediction error rate F is 5%, and the system standby cost coefficient is 110 yuan/MW. The wind power and photovoltaic output prediction curves are shown in fig. 4.
In this embodiment, 5 different peak shaving depth scenes, such as 50%, 55%, 60%, 65%, 70%, etc., are set with reference to the peak shaving standard of the thermal power generating unit in eastern China. According to the example data, a branch-and-bound algorithm is adopted to solve to obtain simulation results of 5 kinds of peak shaving depths shown in figure 5. The actual output conditions of the thermal power generating unit under different peak shaving depths are shown in fig. 6.
The peak-to-valley difference of the system load can be obviously reduced under 3 scenes of 50%, 55% and 60% of peak-to-peak depths of the thermal power generating unit. By combining the economic dispatching index obtained by the method shown in fig. 5, it can be seen that the grid-connected electric quantity of the thermal power generating unit can be remarkably reduced by large-scale wind power and photovoltaic grid connection, so that the operation cost of the system is reduced. However, during the deep peak shaving operation, extra life loss and oil charging cost are generated, and additional environmental cost is generated, and in the case of reducing the power on the grid, the unit power generation cost of the thermal power plant cannot be reduced despite the compensation benefit of government peak shaving auxiliary service.
The grid-connected consumed power of wind power and photovoltaic under deep peak shaving is shown in fig. 7 and 8. Under the condition of considering the power grid constraint, the peak regulation depth and the new energy consumption are in positive correlation, and the larger the peak regulation range is, the larger the new energy consumption is. As can be seen from the graph of FIG. 5, the unit power generation cost is the lowest at 50% peak shaving depth, and the wind and light rejection rates are 16.9% and 30.3%, respectively. When the peak regulation depth is increased by 5 percent, the phenomena of wind abandoning and light abandoning are obviously reduced. The new energy consumption rate at the peak regulation depth of more than 55 percent can reach more than 90 percent. Therefore, when the output of the unit is reduced to be below 45%, the consumption of the new energy is in a relatively ideal range, the ratio of the new energy is increased, and the energy structure of a power grid can be obviously improved.
When deep peak regulation is carried out, the power consumption value in the graph can be referred to control wind power and photovoltaic on-grid power quantity, and the safety and the economy of power grid operation are considered.
The output of the thermal power generating unit under the peak shaving depth of 55% is shown in fig. 9, and it can be obviously seen that the output change of the unit 4 is larger than that of other units, and at the moment, the main peak shaving pressure of the system under the new energy grid connection is born by the unit. And the higher the capacity grade of the unit is, the more flexible the deep peak regulation is, and the higher the running cost and the peak regulation profit compensation of the unit are. As can be known by combining the graph 6, the output of the thermal power generating unit is relatively stable in the scene, the mechanical loss and the operation loss caused by frequent actions of the thermal power generating unit can be reduced, and meanwhile, the new energy consumption is kept at a higher level.
Fig. 10 shows the output situation of each thermal power generating unit under the condition of 60% peak shaving depth, each thermal power generating unit has small fluctuation in 18-24 time period, the increase of the peak shaving depth increases the grid power of wind power and photovoltaic, compared with the load prediction value and the wind power and photovoltaic output prediction value, the photovoltaic output is reduced to 0 in 20 time period, and the wind power output prediction value is gradually increased, so that the thermal power generating unit needs to increase the output to balance power in this time period, which is consistent with the peak situation of the output of the thermal power generating unit in 18-24 time period in fig. 6. Fig. 11 shows the output condition of each thermal power generating unit under 65% of the peak shaving depth, at this time, the minimum output of the thermal power generating unit is reduced to 35% of the rated capacity, and the peak shaving depth is further enlarged and is close to full-rate new energy consumption. Compared with the graph 10, the output of each unit fluctuates sharply, the climbing up and down is more frequent, the peak of electricity utilization is particularly obvious at night, and although the new energy consumption reaches a more ideal state, the safety of the system operation is reduced to a certain extent compared with the 60% peak regulation depth.
In this embodiment, it is assumed that the above 8-machine system is a thermal power enterprise, and on the premise of not considering other costs of the thermal power enterprise, the price of the network electricity quantity of the thermal power enterprise is SpTaking the current scheduling day as an example, calculating the daily income of the thermal power enterprise by taking 0.3 yuan/kW.h as a calculation formula
SR＝(Sp-Sp1)Pw (17)
In the formula, PwThe method comprises the following steps of (1) enabling the thermal power generating unit to surf the internet on the same day; sp1Is the unit cost of electricity generation.
The total daily gain of the thermal power enterprise under different peak shaving depths obtained by the calculation of the formula is shown in fig. 12.
The economic dispatching of the power grid takes the system operation cost as the minimum target, and does not consider other costs added by deep peak shaving of the thermal power generating unit. With the increase of the peak shaving depth, the income of thermal power enterprises is gradually reduced. When the peak shaving depth is increased from 50% to 70%, despite the compensation of the paid peak shaving service, the income of the thermal power enterprise is reduced by 212.1 ten thousand yuan along with the increase of the service life loss and the oil investment cost, and is less than half of the 50% peak shaving depth, and the thermal power enterprise is reluctant to do deep peak shaving according to the scene. And under 3 scenes of peak shaving depths of 55%, 60% and 65%, the income of the thermal power enterprises is respectively reduced by 74.21, 130.21 and 160.50 ten thousand yuan, and the thermal power enterprises can sacrifice the benefits of the thermal power enterprises in the deep peak shaving period by considering the consumption of new energy and the brought environmental benefits.
The above description is only a preferred embodiment of the present invention, and the scope of the present invention is not limited to the above embodiment, but equivalent modifications or changes made by those skilled in the art according to the present disclosure should be included in the scope of the present invention as set forth in the appended claims.

Claims (7)

1. A thermal power generating unit deep peak shaving economic dispatching method under the condition of large-scale new energy grid connection is characterized by comprising the following steps: the method comprises the following steps:
step1, dividing the deep peak shaving of the thermal power generating unit into 2 stages of oil-throwing-free deep peak shaving DPR and oil-throwing deep peak shaving DPRO, and calculating the energy consumption cost of the deep peak shaving operation, wherein the energy consumption cost comprises the coal consumption cost of the thermal power generating unit, the service life loss cost of the unit, the oil-throwing cost and the environmental additional cost, so as to obtain a fixed cost function of the deep peak shaving process of the thermal power generating unit;
step2, calculating deep peak-shaving operation compensation benefits according to compensated peak-shaving service compensation rules of different areas;
step3, introducing the rotating standby cost, and obtaining the rotating standby cost according to the load, the prediction error rates of the wind power and the photovoltaic and the power of the load, the wind power and the photovoltaic at each moment;
step4, according to the analysis of the fixed cost, the operation compensation income and the rotation standby cost in the deep peak shaving process of the thermal power generating unit in the step, obtaining an objective function with the minimum comprehensive operation cost and relevant constraints;
and 5, solving the objective function to obtain a final scheduling scheme.
2. The thermal power generating unit deep peak shaving economic dispatching method under the large-scale new energy grid-connected condition according to claim 1, characterized by comprising the following steps: in step1, the various cost calculations are specifically as follows:
the coal consumption cost is calculated by adopting the consumption characteristic, and the running coal consumption cost is as follows:
Figure FDA0002819453020000011
in the formula, CcThe coal consumption cost of the thermal power generating unit is reduced; pi,tGenerating capacity of the thermal power generating unit; a, b and c are coal consumption characteristic coefficients; scoalIs the coal price;
calculating the unit life loss cost according to the low cycle fatigue characteristic relation of the rotor material, and obtaining the unit life loss cost as follows according to a Manson-coffee formula:
Figure FDA0002819453020000021
in the formula, A is the service life loss cost of the unit; delta is the actual operation loss coefficient, and the loss coefficient of oil injection is larger than that of deep peak shaving without oil injection; suCost for purchasing machines; n is a radical off(Pi,t) Representing the cycle number of the rotor fracturing;
in the DPRO stage, the oil input and consumption cost is as follows:
B(Pi,t)＝Ocost×So (3)
in the formula, B is the oil feeding cost of the unit; o iscostThe fuel consumption is calculated; soIs the oil price;
in the oil feeding depth peak regulation stage, the environment additional cost of the oil feeding depth peak regulation stage is as follows:
Ev＝Ocost×Wp+Sp(Pi,t,Ocost) (4)
in the formula, EvAdding cost to the environment; wpThe unit fuel oil pollution discharge cost; spAnd the pollution discharge penalty function represents the penalty of the pollution discharge exceeding the standard.
3. The thermal power generating unit deep peak shaving economic dispatching method under the large-scale new energy grid-connected condition according to claim 1, characterized by comprising the following steps: in the step1, based on various costs, the fixed cost function in the deep peak regulation process of the thermal power generating unit is as follows:
Figure FDA0002819453020000022
the starting and stopping cost of the unit in a period of time is as follows:
Figure FDA0002819453020000023
the deep peak regulation cost considering the start-stop cost of the unit is as follows:
Figure FDA0002819453020000031
in the formula (f)costThe unit deep peak shaving cost; csThe unit start-stop cost; paThe maximum output of the unit without oil injection depth peak regulation; pbThe maximum output of peak shaving for the oil feeding depth of the unit; pmax、PminRespectively the maximum output and the minimum output of the thermal power generating unit; t is the number of scheduling time segments; n is the number of units; u shapei,tFor the start-stop state and operation of the unit Ui,t1, U at shutdowni,t＝0；Si,upFor the cost of start-up, Si,downFor the cost of down time.
4. The thermal power generating unit deep peak shaving economic dispatching method under the large-scale new energy grid-connected condition according to claim 1, characterized by comprising the following steps: in step2, calculating compensation benefit of deep peak shaving of the thermal power generating unit by taking each 15min as a unit statistical cycle according to an actual scheduling plan, wherein the compensation benefit is calculated as follows:
Figure FDA0002819453020000032
in the formula, CRevTo compensate for the gain; t is the number of compensated peak shaving hours in the j gear; c. CpjCompensating the price for the peak load regulation for the j-th gear; pdejPeaking for the depth of the j-th gearCapacity, the size is the total peak shaver capacity minus the conventional peak shaver capacity.
5. The thermal power generating unit deep peak shaving economic dispatching method under the large-scale new energy grid-connected condition according to claim 1, characterized by comprising the following steps: in step3, introducing a rotary standby cost under deep peak regulation operation, wherein the rotary standby cost is as follows:
Figure FDA0002819453020000033
in the formula, CbySpare cost for spinning; k is a radical ofbA system standby cost factor; l, F, G is the prediction error rate of load, wind power and photovoltaic; plt、Pft、PgtThe t moment load, the wind power and the photovoltaic power are respectively.
6. The thermal power generating unit deep peak shaving economic dispatching method under the large-scale new energy grid-connected condition according to claim 1, characterized by comprising the following steps: in step4, establishing an objective function with the minimum comprehensive operation cost as follows:
min C＝Cs+Cby-CRev (9)
the constraint of the objective function is as follows:
output restraint of the thermal power generating unit:
Pmin≤Pi,t≤Pmax (10)
neglecting the system power balance constraint of network loss:
Figure FDA0002819453020000041
unit climbing restraint:
Ui,t-1Pi,t-1-αdowni≤pi,t≤αupi+Ui,t-1Pi,t-1 (12)
and (3) limiting the start-stop time:
Figure FDA0002819453020000042
and (3) new energy output constraint:
Figure FDA0002819453020000043
and (3) system rotation standby constraint:
Figure FDA0002819453020000044
in the formula: alpha is alphadowni、αupiRespectively limiting the downward climbing rate and the upward climbing rate of the unit i;
Figure FDA0002819453020000051
Figure FDA0002819453020000052
respectively keeping the unit i running and stopping for a time t; t isoni、ToffiRespectively the minimum continuous running time and the minimum shutdown time of the unit i;
Figure FDA0002819453020000053
respectively predicting values of wind power output and photovoltaic output;
Figure FDA0002819453020000054
respectively the maximum output and the minimum output of the unit i in the time period t; pptThe prediction error of the system is calculated by the following formula:
Ppt＝LPlt+FPft+GPgt (16)
7. the thermal power generating unit deep peak shaving economic dispatching method under the large-scale new energy grid-connected condition according to claim 1, characterized by comprising the following steps: in step5, a branch-and-bound method is adopted to seek a global optimal solution, and the steps are as follows:
step 5-1, setting the value f of the optimal solution to be + ∞, and solving a corresponding relaxation problem; judging whether the original relaxation problem is feasible or not, and if an optimal solution meeting the requirements is found, determining the optimal solution of the original problem; otherwise, branching the original problem to seek the optimal solution;
step 5-2, branching; selecting one solution which does not conform to the integer constraint condition from the optimal solutions, and setting the value of the solution as BjWith BjAnd Bj+1 is an upper bound, and two constraint conditions are respectively constructed to form two subproblems;
step 5-3, delimiting; judging whether the sub-problem has an integer solution, if so, finding out a target value of the original relaxation problem and setting the target value as a lower bound of the branch problem;
step 5-4, sequentially selecting the relaxin problems from the queue to be branched to carry out branch solution, and correcting the upper and lower boundaries of the original problem;
step 5-5, according to the pruning principle, cutting off part of the invalid sub-problems;
and 5-6, checking the solutions and targets of all the branches.
CN202011413796.5A 2020-12-07 2020-12-07 Thermal power generating unit deep peak regulation economic dispatching method under large-scale new energy grid-connected condition Pending CN112508401A (en)

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