CN116934033A - Novel provincial power system power supply optimization and time sequence production simulation method - Google Patents
Novel provincial power system power supply optimization and time sequence production simulation method Download PDFInfo
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Abstract
The invention discloses a novel provincial power system power supply optimization and time sequence production simulation method, which relates to the technical field of power system optimization operation, in particular to a novel provincial power system power supply optimization and time sequence production simulation method, and the simulation method comprises the following specific steps: s1, a novel provincial power system production simulation framework; s2, a novel provincial power system unit maintenance optimization model, and S3, a novel provincial power system unit combination optimization model. According to the novel provincial power system power supply optimization and time sequence production simulation method, annual distribution of new energy power generation and system load characteristics are fully considered from the unit overhaul arrangement, so that new energy consumption is effectively promoted; the unit combination optimization solving method is adopted, the unit cluster solving method is further adopted for the medium-scale thermal power unit clusters, the system operation and the coal consumption during start-up and stop are calculated accurately, and the overall optimization is carried out on the coordinated balance of the output among various power supplies.
Description
Technical Field
The invention relates to the technical field of power system optimization operation, in particular to a novel provincial power system power supply optimization and time sequence production simulation method.
Background
The global energy structure is quickened and regulated in the century, the technical level and economy of new energy are greatly improved, the utilization of wind energy and solar energy realizes jump development, and the scale is increased by tens of times. In 2021, the power generation installation of China is 23.8 hundred million kW, and the wind power generation installation and the photovoltaic power generation installation account for 26.7 percent. The novel electric power system is promoted to be constructed, the new energy duty ratio is promoted to be gradually improved, and the large-scale development and the high-quality development of wind power and solar power generation are comprehensively promoted. The new energy sources such as wind power, photovoltaic and the like have obvious time sequence fluctuation characteristics, the problem of digestion is outstanding, and the power generation side and the energy storage power supply are required to be complementarily used for power generation. The pumped storage is the most mature adjusting power supply with optimal economy and large-scale development condition in the prior art, and has good matching effect with wind power, solar power generation, nuclear power, thermal power and the like. The development of pumped storage is accelerated, the urgent requirement of constructing a novel power system taking new energy as a main body is satisfied, the important support for guaranteeing the safe and stable operation of the power system is satisfied, and the important guarantee for the large-scale development of renewable energy is satisfied.
In the novel provincial power system taking new energy as a main body, the power supply mainly comprises wind power, photovoltaic, thermal power, hydropower, pumped storage and the like. Planning level year, under the condition that wind power, photovoltaic, water and electricity installation capacity confirm, the main problem that pumped storage planning needs to solve: (1) what the optimal configuration capacity of pumped storage is; (2) what the system coal saving amount of the pumped storage power station is. Developing power system power planning and production simulations is the core and key to solving the above problems.
The production simulation is the basis of power system planning and operation optimization, and the power generation amount, the fuel consumption and the emission of each unit are predicted by simulating the operation process of the power system, so that a basis is provided for making a reasonable power supply planning scheme or operation plan. Sequential production simulations fall into two categories: (1) heuristic time sequence production simulation method; (2) a time sequence production simulation method based on an optimization solving technology. The heuristic time sequence production simulation method converts a time sequence load curve into a continuous load curve, and simulates the power generation scheduling process of the power system by reasonably arranging the positions and the output levels of various power supplies on the continuous load curve so as to calculate the total production cost of the system. The disadvantage of this method is evident in the new power system: the influence of the load and the new energy output characteristic change on the technical and economic indexes of the system operation is difficult to accurately reflect; as the power supply structure becomes more complex, the difficulty of designing a coordination balance strategy among different types of power supply units is increased. The core of the time sequence production simulation method based on the optimization solving technology is a unit combination model which aims at the minimum system operation cost, namely, the system operation constraint and the unit operation constraint are met, and the starting and stopping states and the output sizes of all units of various power supplies are decided through the optimization solving technology. The unit combination problem is a nonlinear programming problem comprising integer variables and continuous variables, and a unit combination model based on mixed integer programming (MixedIntegerProgramming, MIP) is widely used.
The method is limited by the existing mathematical method and technical capability, the machine set combination model based on MIP is still difficult to be directly applied to long-term time sequence production simulation in a provincial power system, and a short-period (24 or 168 time periods) rolling solution technology is generally adopted for sequential solution, but the method cannot solve the problem of cross-week and cross-month of machine set overhaul and the like. The scholars put forward a multi-time scale production simulation method containing a large-scale clean power system, the long-term simulation considers the seasonal distribution of clean energy sources, a conventional unit maintenance plan is formulated, the short-term simulation takes the maintenance plan as a boundary, and the unit combination problem is finely simulated. For a provincial power system, the number of various conventional power supply units exceeds hundred, even if a short-period rolling solving technology is adopted for solving, the solving scale is still larger, and the unit clustering technology can reduce the solving scale, accelerate the solving speed and has higher precision. The unit cluster converts binary variables of large-scale unit combinations into single integer variables, so that the solving scale is reduced, and the unit cluster is widely applied to the aspects of long-term operation management, power supply development planning and the like of a power system.
In the novel provincial power system power supply planning and time sequence production simulation, the unit maintenance and the unit combination problem need to be considered at the same time, and the calculation period is 1 year. The unit combination problem calculation time period step length is 1h, and the annual calculation time period total number is 8760. Because the provincial power system has complex power supply composition and huge unit quantity, even if a unit cluster technology is adopted, the unit maintenance and unit combination problem can be solved at the same time.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a novel power supply optimization and time sequence production simulation method of a provincial power system, and solves the problems that the power supply composition of the provincial power system is complex, the number of units is huge, and even if a unit cluster technology is adopted, the unit overhaul and unit combination problem is still difficult to solve in the background art.
In order to achieve the above purpose, the invention is realized by the following technical scheme: the power supply optimization and time sequence production simulation method of the novel provincial power system comprises the following specific steps:
s1, a novel provincial power system production simulation framework;
(1) The simulation framework is constructed, and the novel provincial power system production simulation consists of a unit maintenance optimization model and a unit combination optimization model;
the main function of the unit maintenance optimization model is to determine a unit maintenance plan according to the annual distribution and load characteristics of new energy output, promote new energy consumption, ensure the even distribution of the system in idle capacity year, and determine the minimum required capacity of the system for thermal power by giving a pumped storage installed capacity scheme after determining the new energy and hydropower installation scale in planning horizontal year; the main function of the unit combination optimization model is to develop an hour-by-hour production simulation according to boundary conditions determined by an overhaul plan, and calculate the annual operation index of the system on the basis of 8760 hours of the whole year; thus, the two are in a unidirectional and loose coupling mode;
Calculating the unit overhaul optimization model in a weekly period to obtain a daily unit overhaul state in an annual period; the calculation period of the unit combination optimization model is 1d, the calculation period is 1h, and unit combination optimization calculation is carried out by taking a daily unit maintenance plan as a boundary; taking the state of the unit at the last time of the previous day as the initial state of the unit at the beginning time of the current day, and performing sequential and rolling simulation day by day;
(2) In the medium-scale computing cluster method, in the unit combination optimization, units with the same or similar unit characteristics are generally formed into a cluster unit, so that the computing efficiency can be improved, but the difficulty exists in the process of overhauling and optimizing the coupling with the units; when the maintenance optimization calculation is performed, if a single unit is taken as a calculation unit, the calculation amount is too large, the solution is difficult, and the cluster units in the unit combination optimization cannot be directly coupled; if a large-scale cluster unit in the unit combination is taken as a computing unit: (1) if the binary integer variable is adopted to represent the overhaul state, the cluster unit has large scale and unreasonable overhaul; (2) if the maintenance state is represented by an integer variable, maintenance allocation in the cluster unit cannot be accurately described, and the maintenance continuity constraint of a single unit cannot be met;
The invention provides a medium-scale unit clustering method, which aims to avoid oversized cluster units, and divides a large-scale cluster unit into a plurality of medium-scale cluster units, wherein the same cluster units are adopted in a unit maintenance optimization model and a unit combination optimization model; in the overhaul optimization model, binary integer variables are adopted to represent overhaul states, and the cluster scale should meet overhaul site constraint;
s2, a novel provincial power system unit maintenance optimization model, wherein in the novel provincial power system taking new energy as a main body, the unit maintenance optimization model considers the annual distribution problem of clean energy on one hand and the annual distribution problem of system idle capacity on the other hand; in power supply planning, after new energy and water installation capacity are determined, the optimal allocation capacity of pumped storage is determined by adopting multi-scheme comparison, namely, a plurality of pumped storage capacity schemes are assumed, each scheme meets the system load requirement by increasing and decreasing the thermal power capacity, and each combined scheme is required to have minimum system coal consumption and minimum idle capacity; in order to reduce the solving scale, unit overhaul optimization usually adopts a block load curve, but the method cannot consider the time sequence fluctuation of the load and cannot fully consider the overhaul arrangement of the pumped storage unit;
From a macroscopic view, the overhaul of the thermal power generating unit is arranged as much as possible in the period of large new energy generating capacity, so that the new energy is favorably consumed; arranging water and electricity and pumped storage units to overhaul as much as possible in a period with smaller peak-valley difference of residual load of the system, and meeting the requirement of the system on peak regulation capacity while promoting new energy consumption; the final aim of the strategy is to reduce the coal consumption of the system operation; the invention provides a novel provincial power system unit maintenance optimization model, which takes the minimum idle capacity of a system as an objective function, takes the balance of the power of the system and the balance of peak shaving capacity as constraint conditions, and simultaneously considers constraint conditions such as unit maintenance continuity constraint, maintenance site constraint, maintenance period constraint and the like;
(1) And (3) calculating an objective function of the overhaul optimization model, wherein the minimum idle capacity of the system is adopted as the objective function, and the expression is as follows:
wherein: i is the serial number of the cluster unit; n (N) th 、N h 、N p Respectively the cluster numbers of thermal power, hydroelectric power and pumped storage units in the system;the installed capacities of the thermal power, hydroelectric power and pumped storage cluster unit i are respectively; />The overhaul state of the thermal power, hydroelectric power and pumped storage cluster unit i in the period d is 0-1 integer variable, 1 represents overhaul, and 0 represents non-overhaul; />To calculate the maximum value of the residual load in the period, D t For t period system load demand, D d，max For maximum system load in period d, r is a standby coefficient, N w 、N pv The number of wind power and photovoltaic cluster units is +.>The active power output of the wind power and photovoltaic cluster unit i in the period t is respectively calculated, and d is the overhauling optimization calculation period; y is a calculation period of 8760 hours in the whole year;
(2) And calculating a maintenance optimization model target constraint condition, wherein the calculation is specifically as follows:
a) System power balance constraint:
b) System peak shaving capacity balance constraint:
after large-scale new energy grid connection, the peak-valley difference of the residual load of the system is further increased, if the maintenance arrangement is unreasonable, a large amount of new energy sources can be abandoned, and the coal consumption of the system is increased; the hydropower and pumped storage unit overhaul can be arranged in a period with larger peak-valley difference of residual load of the system through peak regulation capacity balance constraint, and the expression is as follows:
wherein: gamma ray i The average peak regulation depth coefficient of the cluster thermal power generating unit i;respectively calculating the maximum value and the minimum value of the residual load in the period;
c) Other constraints
The overhaul state of the unit is 0, 1 integer constraint, 0 represents a non-overhaul state, 1 represents an overhaul state; maintenance continuity constraint, and continuously stopping during maintenance of each unit; the maintenance site is restricted, and the number of maintenance units in the same period is mainly controlled; the maintenance period is constrained, and the non-maintenance of the hydroelectric generating set in the flood period and the non-maintenance of the heating generating set in the winter heating period are mainly controlled;
S3, a novel provincial power system unit combination optimization model;
(1) The method comprises the steps of combining optimization model function calculation, wherein the model takes the minimum total cost as an objective function, and the total cost comprises the sum of thermal power generation cost of a system and economic losses of waste wind, waste light and waste water;
wherein: f (F) i (. Cndot.) is the operating cost function of the cluster thermal power unit i;active output of the cluster thermal power generating unit i in a period t; />The method comprises the steps of (1) starting cost of a cluster thermal power generating unit i in a period t; />The shutdown cost of the cluster thermal power generating unit i in the period t is calculated; t is the total period number in the power generation planning period, t=24 hours; n (N) th The number of the thermal power generating units is the number of the cluster thermal power generating units in the system; n (N) h 、N w 、N pv The number of the hydropower, wind power and photovoltaic cluster units in the system is respectively; />The method comprises the steps of respectively discarding the water electric quantity, the wind electric quantity and the light electric quantity of a cluster unit i in a period t; k (K) h 、K w 、K pv The cost of the unit waste water electric quantity, the unit waste wind electric quantity and the unit waste light electric quantity are respectively;
(2) And calculating constraint conditions of the combined optimization model, wherein the constraint conditions mainly comprise: load balance constraint, unit output upper and lower limit constraint, energy storage constraint, hydropower station electric quantity constraint and reserve capacity constraint;
(3) A cluster of thermal power generating units;
a) A cluster thermal power unit combination state;
b) Output constraint of the cluster unit;
c) Thermal power minimum start-stop constraint;
d) Running cost.
Optionally, the medium-scale unit clustering method in the step S1 can directly couple unit overhaul and unit combination optimization, ensure continuity of single unit overhaul, reduce solving scale, improve solving speed and have higher precision;
taking thermal power as an example, the method for determining the cluster size of the unit is as follows:
in the method, in the process of the invention,the installed capacity of the thermal power cluster unit i; c (C) th The total thermal power installed capacity of the system; />D, the thermal power overhaul time length is d; n is a positive integer, and is limited by maintenance sites, calculation scale and the like; []To round the symbol.
Optionally, in the step S2, the calculation of the objective function of the overhaul optimization model is to determine the minimum thermal power capacity required by the system according to the given pumped storage capacity under the conditions of given system load, wind power and photovoltaic output, so as to analyze the minimum thermal power capacity required by the pumped storage, and when the thermal power capacity is smaller than the minimum thermal power capacity required by the system, the overhaul plan of each power supply cannot meet the operation requirement of the system anyway.
Optionally, the specific content of the calculation of the constraint condition of the combined optimization model in S3 is as follows:
(1) Load balancing constraints
The sum of the output of each power supply period of the power system is equal to the load of the system period, and the expression is as follows:
Wherein: d (D) t System load for period t;
(2) Upper and lower limit constraint of unit output
The device is suitable for power supplies such as wind power, photovoltaic, thermal power, hydroelectric power, pumped storage and the like; for wind power and photovoltaic output, the upper limit is the maximum output of the unit under the corresponding wind and light conditions, and the lower limit is 0; for hydropower, the upper limit is the expected force and the lower limit is the minimum technical force; for pumped storage, the upper limit is the installed capacity, and the lower limit is 0; for thermal power, the upper limit is the installed capacity, the lower limit is the minimum technical output, and the expression is as follows:
I i,t P i,min ≤P i,t ≤I i,t P i,max (7)
wherein: i=1, 2, …, N; t=1, 2, …, T;
(3) Energy storage constraint
The energy storage constraint expression is as follows:
wherein: s is S s (t) is the state of charge of the energy storage system during period t;and->Charging and discharging power of the energy storage system in the t period respectively; η (eta) c Representing the energy storage conversion efficiency of the energy storage system, and taking 0.75; s is S max And S is min The upper limit and the lower limit of the energy storage state of the energy storage system are respectively;
(4) Hydropower station electric quantity constraint
Considering the electric quantity constraint determined by the average output of different hydrologics, the expression is as follows:
in the method, in the process of the invention,average output of the clustered hydroelectric generating set;
(5) Spare capacity constraint
The reserve rate of the rotary reserve and the load reserve is 8 percent, and the rotary reserve and the load reserve are jointly born by thermal power, hydropower and pumped storage; the reserve rate of cold standby is 5%, all the reserve rate is borne by the thermal power unit, and the expression is as follows:
Wherein:respectively carrying out rotary standby and load standby for t time periods of the cluster thermal power, hydroelectric power and pumped storage i units; />And (5) cold standby for the cluster thermal power i unit t period.
Optionally, the combination state of the cluster thermal power generating unit in S3 is specifically as follows:
group thermal power unit i is composed of J i The similar units are formed, and the constraint expression of the change of the starting capacity between adjacent time periods is as follows:
wherein:the starting capacities of the cluster thermal power generating units i at the t-1 period and the t period are respectively,I i (t)∈{0,J i }，C i is the average single machine installed capacity of the cluster thermal power unit I, I i (t) is a boot integer variable; />Starting capacity of t-period cluster thermal power generating unit i +.>U i (t)∈{0,J i }；/>For the shutdown capacity of t period cluster thermal power generating unit i,/>D i (t)∈{0,J i }。
The unit state variable constraint conditions are as follows:
0≤I i (t),U i (t),D i (t)≤J i (12)
optionally, the cluster unit output constraint in S3 is specifically as follows:
output power P of cluster thermal power generating unit i in period t i (t) satisfying the constraint:
wherein alpha is i And (t) is the minimum technical output rate of the cluster thermal power generating unit i, and is obtained by weighted average of the minimum technical output rate of a single machine.
Optionally, the thermal power minimum start-up and shutdown constraint in S3 is specifically as follows:
after the thermal power generating unit is connected with the power grid, the thermal power generating unit can be stopped only after a certain running time is ensured, and after the thermal power generating unit is stopped, the power generating unit can be connected with the power grid only after a certain stopping time is ensured, and the minimum starting and stopping constraint expression of the cluster unit is as follows:
In the formula, MUT i 、MDT i For minimum start-up and shut-down time.
Optionally, the running cost in S3 is specifically as follows:
the operation cost comprises the power generation cost F i P (t) start-up cost F i U (t) and shutdown costs F i D (t); the power generation cost expression is as follows:
in the method, in the process of the invention,the running coal consumption is the minimum output of the unit starting capacity technology; />A linear power generation coal consumption slope weighted average value of the cluster unit i;
the starting cost and the shutdown cost function of the cluster unit i are as follows:
in the parameters ofAnd->The coal consumption corresponding to the unit start-up capacity and the unit stop capacity are respectively indicated.
The invention provides a novel provincial power system power supply optimization and time sequence production simulation method, which has the following beneficial effects:
the novel provincial power system power supply optimization and time sequence production simulation method designs a double-layer coupling frame of a provincial power system unit overhaul plan and a unit combination, and provides a medium-scale unit clustering method, so that unit overhaul and unit combination optimization can be directly coupled, continuity of single unit overhaul is ensured, solving scale is reduced, and solving speed is improved. The novel provincial power system maintenance optimization model is provided, the minimum idle capacity of the system is taken as an objective function, and the constraints such as the power balance constraint, the peak shaving capacity constraint, the maintenance continuity constraint, the maintenance site constraint, the maintenance period constraint and the like of the system are considered. Under the condition of determining the capacity of other power sources, calculating the required capacity of the thermal power;
The overhaul arrangement of various power supply units fully considers annual distribution of new energy generating capacity and system load characteristics, and promotes new energy consumption. In the unit combination optimization model, the minimum system operation cost is taken as an objective function, the wind, light and water discarding cost is considered, a unit cluster solving method is further adopted for the medium-scale thermal power unit clusters, and the thermal power operation and the unit start-stop coal consumption are accurately calculated. In the time sequence production simulation of the provincial power system, the calculation result of the method is obviously superior to that of a heuristic method, and the method can carry out global optimization on the coordinated balance of the output among various power supplies.
Drawings
FIG. 1 is a schematic diagram of a double-layer coupling frame of a unit maintenance plan and a unit combination of a provincial power system in a novel provincial power system power supply optimization and time sequence production simulation method;
FIG. 2 is a schematic diagram of a medium-scale unit cluster in the novel provincial power system power optimization and sequential production simulation method;
FIG. 3 is a schematic diagram of month-by-month load and external power delivery in the novel provincial power system power optimization and sequential production simulation method;
FIG. 4 is a schematic diagram of the new monthly energy generation in the novel provincial power system power optimization and sequential production simulation method;
FIG. 5 is a schematic diagram of a maintenance plan of a cluster thermal power unit in the novel provincial power system power supply optimization and time sequence production simulation method;
FIG. 6 is a schematic diagram of a clustered hydro-power generating set overhaul plan in the novel provincial power system power supply optimization and time series production simulation method;
FIG. 7 is a schematic diagram of a clustered pumped storage unit overhaul plan in the novel provincial power system power optimization and sequential production simulation method;
FIG. 8 is a schematic diagram of various power supply output processes in the typical month of the summer of the design level year (2035 years) in the novel provincial power system power supply optimization and time sequence production simulation method;
fig. 9 is a schematic diagram of various power output processes in the typical month of winter in the design level (2035 years) in the power supply optimization and time sequence production simulation method of the novel provincial power system.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "connected," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Example 1
The invention provides a technical scheme that: the power supply optimization and time sequence production simulation method of the novel provincial power system comprises the following specific steps:
s1, a novel provincial power system production simulation framework;
(1) The simulation framework is constructed, and the novel provincial power system production simulation consists of a unit maintenance optimization model and a unit combination optimization model;
the main function of the unit maintenance optimization model is to determine a unit maintenance plan according to the annual distribution and load characteristics of new energy output, promote new energy consumption, ensure the even distribution of the system in idle capacity year, and determine the minimum required capacity of the system for thermal power by giving a pumped storage installed capacity scheme after determining the new energy and hydropower installation scale in planning horizontal year; the main function of the unit combination optimization model is to develop an hour-by-hour production simulation according to boundary conditions determined by an overhaul plan, and calculate the annual operation index of the system on the basis of 8760 hours of the whole year; thus, the two are in a unidirectional and loose coupling mode;
calculating the unit overhaul optimization model in a weekly period to obtain a daily unit overhaul state in an annual period; the calculation period of the unit combination optimization model is 1d, the calculation period is 1h, and unit combination optimization calculation is carried out by taking a daily unit maintenance plan as a boundary; taking the state of the unit at the last time of the previous day as the initial state of the unit at the beginning time of the current day, and performing sequential and rolling simulation day by day;
(2) In the medium-scale computing cluster method, in the unit combination optimization, units with the same or similar unit characteristics are generally formed into a cluster unit, so that the computing efficiency can be improved, but the difficulty exists in the process of overhauling and optimizing the coupling with the units; when the maintenance optimization calculation is performed, if a single unit is taken as a calculation unit, the calculation amount is too large, the solution is difficult, and the cluster units in the unit combination optimization cannot be directly coupled; if a large-scale cluster unit in the unit combination is taken as a computing unit: (1) if the binary integer variable is adopted to represent the overhaul state, the cluster unit has large scale and unreasonable overhaul; (2) if the maintenance state is represented by an integer variable, maintenance allocation in the cluster unit cannot be accurately described, and the maintenance continuity constraint of a single unit cannot be met;
the invention provides a medium-scale unit clustering method, which aims to avoid oversized cluster units, and divides a large-scale cluster unit into a plurality of medium-scale cluster units, wherein the same cluster units are adopted in a unit maintenance optimization model and a unit combination optimization model; in the overhaul optimization model, binary integer variables are adopted to represent overhaul states, and the cluster scale should meet overhaul site constraint;
The medium-scale unit cluster method can directly couple unit overhaul and unit combination optimization, ensure the continuity of single unit overhaul, reduce the solving scale, improve the solving speed and have higher precision;
taking thermal power as an example, the method for determining the cluster size of the unit is as follows:
in the method, in the process of the invention,the installed capacity of the thermal power cluster unit i; c (C) th The total thermal power installed capacity of the system; />D, the thermal power overhaul time length is d; n is a positive integer, and is limited by maintenance sites, calculation scale and the like; []Is a rounding symbol;
s2, a novel provincial power system unit maintenance optimization model, wherein in the novel provincial power system taking new energy as a main body, the unit maintenance optimization model considers the annual distribution problem of clean energy on one hand and the annual distribution problem of system idle capacity on the other hand; in power supply planning, after new energy and water installation capacity are determined, the optimal allocation capacity of pumped storage is determined by adopting multi-scheme comparison, namely, a plurality of pumped storage capacity schemes are assumed, each scheme meets the system load requirement by increasing and decreasing the thermal power capacity, and each combined scheme is required to have minimum system coal consumption and minimum idle capacity; in order to reduce the solving scale, unit overhaul optimization usually adopts a block load curve, but the method cannot consider the time sequence fluctuation of the load and cannot fully consider the overhaul arrangement of the pumped storage unit;
From a macroscopic view, the overhaul of the thermal power generating unit is arranged as much as possible in the period of large new energy generating capacity, so that the new energy is favorably consumed; arranging water and electricity and pumped storage units to overhaul as much as possible in a period with smaller peak-valley difference of residual load of the system, and meeting the requirement of the system on peak regulation capacity while promoting new energy consumption; the final aim of the strategy is to reduce the coal consumption of the system operation; the invention provides a novel provincial power system unit maintenance optimization model, which takes the minimum idle capacity of a system as an objective function, takes the balance of the power of the system and the balance of peak shaving capacity as constraint conditions, and simultaneously considers constraint conditions such as unit maintenance continuity constraint, maintenance site constraint, maintenance period constraint and the like;
(1) And (3) calculating an objective function of the overhaul optimization model, wherein the minimum idle capacity of the system is adopted as the objective function, and the expression is as follows:
wherein: i is the serial number of the cluster unit; n (N) th 、N h 、N p Respectively the cluster numbers of thermal power, hydroelectric power and pumped storage units in the system;the installed capacities of the thermal power, hydroelectric power and pumped storage cluster unit i are respectively; />The overhaul state of the thermal power, hydroelectric power and pumped storage cluster unit i in the period d is 0-1 integer variable, 1 represents overhaul, and 0 represents non-overhaul; />To calculate the maximum value of the residual load in the period, D t For t period system load demand, D d，max For maximum system load in period d, r is a standby coefficient, N w 、N pv The number of wind power and photovoltaic cluster units is +.>The active power output of the wind power and photovoltaic cluster unit i in the period t is respectively calculated, and d is the overhauling optimization calculation period; y is a calculation period of 8760 hours in the whole year;
the maintenance optimization model objective function calculation is to determine the minimum thermal power capacity required by the system according to the given pumped storage capacity under the condition of given system load, wind power and photovoltaic output upper limit, so as to analyze the pumped storage capacity which can replace the thermal power capacity, and when the thermal power capacity is smaller than the minimum thermal power capacity required by the system, the maintenance plans of all power supplies cannot meet the system operation requirement in any way through optimization.
(2) And calculating a maintenance optimization model target constraint condition, wherein the calculation is specifically as follows:
a) System power balance constraint:
b) System peak shaving capacity balance constraint:
after large-scale new energy grid connection, the peak-valley difference of the residual load of the system is further increased, if the maintenance arrangement is unreasonable, a large amount of new energy sources can be abandoned, and the coal consumption of the system is increased; the hydropower and pumped storage unit overhaul can be arranged in a period with larger peak-valley difference of residual load of the system through peak regulation capacity balance constraint, and the expression is as follows:
Wherein: gamma ray i The average peak regulation depth coefficient of the cluster thermal power generating unit i;respectively calculating the maximum value and the minimum value of the residual load in the period;
c) Other constraints
The overhaul state of the unit is 0, 1 integer constraint, 0 represents a non-overhaul state, 1 represents an overhaul state; maintenance continuity constraint, and continuously stopping during maintenance of each unit; the maintenance site is restricted, and the number of maintenance units in the same period is mainly controlled; the maintenance period is constrained, and the non-maintenance of the hydroelectric generating set in the flood period and the non-maintenance of the heating generating set in the winter heating period are mainly controlled;
s3, a novel provincial power system unit combination optimization model;
(1) The unit combination optimization model function calculation is carried out, the model takes the minimum total cost as an objective function, and the total cost comprises the sum of the thermal power generation cost of the system and the economic losses of wind discarding, light discarding and water discarding;
wherein: f (F) i (. Cndot.) is the operating cost function of the cluster thermal power unit i;active output of the cluster thermal power generating unit i in a period t; />The method comprises the steps of (1) starting cost of a cluster thermal power generating unit i in a period t; />The shutdown cost of the cluster thermal power generating unit i in the period t is calculated; t is the total period number in the power generation planning period, t=24 hours; n (N) th The number of the thermal power generating units is the number of the cluster thermal power generating units in the system; n (N) h 、N w 、N pv The number of the hydropower, wind power and photovoltaic cluster units in the system is respectively; / >The method comprises the steps of respectively discarding the water electric quantity, the wind electric quantity and the light electric quantity of a cluster unit i in a period t; k (K) h 、K w 、K pv The cost of the unit waste water electric quantity, the unit waste wind electric quantity and the unit waste light electric quantity are respectively;
(2) And calculating constraint conditions of the unit combination optimization model, wherein the constraint conditions mainly comprise: load balance constraint, unit output upper and lower limit constraint, energy storage constraint, hydropower station electric quantity constraint and reserve capacity constraint;
the specific contents of the constraint condition calculation of the unit combination optimization model are as follows:
(1) Load balancing constraints
The sum of the output of each power supply period of the power system is equal to the load of the system period, and the expression is as follows:
wherein: d (D) t System load for period t;
(2) Upper and lower limit constraint of unit output
The device is suitable for power supplies such as wind power, photovoltaic, thermal power, hydroelectric power, pumped storage and the like; for wind power and photovoltaic output, the upper limit is the maximum output of the unit under the corresponding wind and light conditions, and the lower limit is 0; for hydropower, the upper limit is the expected force and the lower limit is the minimum technical force; for pumped storage, the upper limit is the installed capacity, and the lower limit is 0; for thermal power, the upper limit is the installed capacity, the lower limit is the minimum technical output, and the expression is as follows:
I i,t P i,min ≤P i,t ≤I i,t P i,max (7)
wherein: i=1, 2, …, N; t=1, 2, …, T;
(3) Energy storage constraint
The energy storage constraint expression is as follows:
wherein: s is S s (t) is the state of charge of the energy storage system during period t;and->Charging and discharging power of the energy storage system in the t period respectively; η (eta) c Representing the energy storage conversion efficiency of the energy storage system, and taking 0.75; s is S max And S is min Respectively the upper limit and the lower limit of the energy storage state of the energy storage system；
(4) Hydropower station electric quantity constraint
Considering the electric quantity constraint determined by the average output of different hydrologics, the expression is as follows:
in the method, in the process of the invention,average output of the clustered hydroelectric generating set;
(5) Spare capacity constraint
The reserve rate of the rotary reserve and the load reserve is 8 percent, and the rotary reserve and the load reserve are jointly born by thermal power, hydropower and pumped storage; the reserve rate of cold standby is 5%, all the reserve rate is borne by the thermal power unit, and the expression is as follows:
/>
wherein:respectively carrying out rotary standby and load standby for t time periods of the cluster thermal power, hydroelectric power and pumped storage i units; />The method comprises the steps of (1) reserving cold born by a cluster thermal power i unit in a t period;
(3) A cluster of thermal power generating units;
a) The cluster thermal power generating unit combination state is specifically as follows:
group thermal power unit i is composed of J i The similar units are formed, and the constraint expression of the change of the starting capacity between adjacent time periods is as follows:
wherein:the starting capacities of the cluster thermal power generating units i at the t-1 period and the t period are respectively, I i (t)∈{0,J i }，C i Is the average single machine installed capacity of the cluster thermal power unit I, I i (t) is a boot integer variable; />Starting capacity of t-period cluster thermal power generating unit i +.>U i (t)∈{0,J i }；/>For the shutdown capacity of t period cluster thermal power generating unit i,/>D i (t)∈{0,J i }。
The unit state variable constraint conditions are as follows:
0≤I i (t),U i (t),D i (t)≤J i (12)
b) The output constraint of the cluster unit is specifically as follows:
output power P of cluster thermal power generating unit i in period t i (t) satisfying the constraint:
wherein alpha is i (t) is the minimum technical output rate of the cluster thermal power generating unit i, and is obtained by weighted average of the minimum technical output rate of a single machine;
c) Thermal power minimum start-stop constraint, the thermal power minimum start-stop constraint is specifically as follows:
after the thermal power generating unit is connected with the power grid, the thermal power generating unit can be stopped only after a certain running time is ensured, and after the thermal power generating unit is stopped, the power generating unit can be connected with the power grid only after a certain stopping time is ensured, and the minimum starting and stopping constraint expression of the cluster unit is as follows:
in the formula, MUT i 、MDT i For minimum start-up and shut-down times;
d) The operation cost is specifically as follows:
the operation cost comprises the power generation cost F i P (t) start-up cost F i U (t) and shutdown costs F i D (t); the power generation cost expression is as follows:
in the method, in the process of the invention,the running coal consumption is the minimum output of the unit starting capacity technology; />A linear power generation coal consumption slope weighted average value of the cluster unit i;
The starting cost and the shutdown cost function of the cluster unit i are as follows:
in the parameters ofAnd->The coal consumption corresponding to the unit start-up capacity and the unit stop capacity are respectively indicated. />
Example two
Referring to fig. 1 to 9, a certain provincial power system is designed to have a highest load of 29151MW in the horizontal year (2035), a power consumption of 1622 million kw.h, a maximum power transmission capacity of 30000MW and a annual power transmission capacity of 1797 million kw.h; the provincial power system power supply comprises wind power, photovoltaic, thermal power, hydroelectric power, pumped storage and the like; the method comprises the steps of planning the horizontal annual wind power installation capacity 60150MW, the photovoltaic installation capacity 31470MW and the hydroelectric installation capacity 2330MW; the installed capacity of the pumped storage is determined to be 19500MW (including 2400MW of the built and the built pumped storage and 17100MW of the newly added power supply) through the optimized configuration of the system power supply; the new energy accounts for 59.4% of the system planning power installation, the proportion is higher, and the pumped storage power station plays a very important role in the absorption of new energy in the power system; the total of the load of the local network and the electricity quantity of the outgoing year is 3419 hundred million kW.h in the planned horizontal year, and the electricity quantity of each month is shown in figure 3; the wind power annual installation utilizes the hours of 2300h, the photovoltaic annual installation utilizes the hours of 1600h, and the month-by-month power generation capacity of the horizontal annual new energy (wind power and photovoltaic) is planned, as shown in the figure 4;
As can be seen from fig. 4, the load and the amount of electricity delivered are 7 months at the maximum value in year, and 2 months at the minimum value in year; the power generation amount of the new energy is 10 months as the maximum value in the year, and 7 months as the minimum value in the year; therefore, the annual distribution of the new energy power generation amount and the required electric quantity of the system have poor matching property;
through maintenance optimization model calculation, when the thermal power capacity of the planned horizontal year system is 40900MW, the power balance constraint and the peak shaving capacity balance constraint are met, and the idle capacity of the system is minimum; according to the limit of the installed capacity and the calculation condition of each power supply, the thermal power is divided into 20 cluster units, the hydropower is divided into 9 cluster units, and the pumped storage is divided into 15 cluster units; the thermal power overhaul time length is 45d, the overhaul time length of the hydroelectric power and pumped storage unit is 30d, the non-overhaul of the thermoelectric unit in the heating period and the non-overhaul of the hydroelectric power unit in the flood period are considered, the continuity of unit overhaul is ensured, various power supply cluster unit overhaul plans are obtained, and the result is shown in figure 5;
as can be seen from the figure, the non-overhaul constraint of the heating unit in the heating period is that the heating unit is mainly concentrated in the overhaul in summer and autumn, and the non-heating unit is mainly concentrated in the overhaul in the heating period; 2 months are the lowest annual load period, 3-5 months and 10 months have larger new energy generating capacity, and the thermal power generating unit has larger maintenance capacity, thereby being beneficial to new energy consumption; the new energy power generation amount is large in month, the maintenance capacity of the pumped storage unit is small, and the adjustment of the new energy output is facilitated;
Various power supply overhaul scheduling determined by the unit overhaul optimization model fully considers the annual distribution characteristics of load and new energy power generation, and intensively overhauls the thermal power unit by using the annual low-load period and the months with large new energy power generation, the pumped storage unit overhaul is arranged as little as possible in the month with large new energy power generation, the new energy consumption is promoted, and meanwhile, as much pumped storage capacity as possible is reserved for peak regulation and valley filling of the system, so that the overall operation cost of the system is reduced;
solving a combined optimization problem of the unit in 8760 hours of planning level by taking the calculation result of the overhaul optimization model as a boundary condition, and comparing the calculated result with the calculation result of a heuristic time sequence production simulation method, wherein the result is shown in a table 1;
table 1 comparison of simulation results
As can be seen from table 1, in terms of new energy power rejection rate, the new energy power rejection rate of the heuristic method is 10.86%, the new energy power rejection rate of the method is 9.76%, and the electric quantity of the multi-absorption new energy power supply is 21 hundred million kW.h; in the aspect of water-electricity discarding rate, the heuristic method is 0.6%, the method is 0, and the water-electricity discarding amount is reduced by 0.22 hundred million kW.h; in terms of the utilization hours of the thermal power annual installation, the heuristic method is 4495h, and the method is 4416h and is reduced by 79h compared with the heuristic method; the water-storage annual installation uses the hour aspect, the heuristic method is 1126 hours, the method is 942 hours, and 184 hours are reduced compared with the heuristic method; in the aspect of system annual coal consumption, the heuristic method is 5925 ten thousand t, the method is 5707 ten thousand t, 218 ten thousand t is reduced compared with the heuristic method, and the energy-saving benefit is very remarkable;
Typical months of summer (7 months) and winter (12 months) are selected, and a schematic diagram of the hour-by-hour output process of various power supplies in the design level year is drawn, as shown in fig. 8 and 9.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (8)
1. The novel provincial power system power supply optimization and time sequence production simulation method is characterized by comprising the following steps of: the simulation method comprises the following specific steps:
s1, a novel provincial power system production simulation framework;
(1) The simulation framework is constructed, and the novel provincial power system production simulation consists of a unit maintenance optimization model and a unit combination optimization model;
the main function of the unit maintenance optimization model is to determine a unit maintenance plan according to the annual distribution and load characteristics of new energy output, promote new energy consumption, ensure the even distribution of the system in idle capacity year, and determine the minimum required capacity of the system for thermal power by giving a pumped storage installed capacity scheme after determining the new energy and hydropower installation scale in planning horizontal year; the main function of the unit combination optimization model is to develop an hour-by-hour production simulation according to boundary conditions determined by an overhaul plan, and calculate the annual operation index of the system on the basis of 8760 hours of the whole year; thus, the two are in a unidirectional and loose coupling mode;
Calculating the unit overhaul optimization model in a weekly period to obtain a daily unit overhaul state in an annual period; the calculation period of the unit combination optimization model is 1d, the calculation period is 1h, and unit combination optimization calculation is carried out by taking a daily unit maintenance plan as a boundary; taking the state of the unit at the last time of the previous day as the initial state of the unit at the beginning time of the current day, and performing sequential and rolling simulation day by day;
(2) In the medium-scale computing cluster method, in the unit combination optimization, units with the same or similar unit characteristics are generally formed into a cluster unit, so that the computing efficiency can be improved, but the difficulty exists in the process of overhauling and optimizing the coupling with the units; when the maintenance optimization calculation is performed, if a single unit is taken as a calculation unit, the calculation amount is too large, the solution is difficult, and the cluster units in the unit combination optimization cannot be directly coupled; if a large-scale cluster unit in the unit combination is taken as a computing unit: (1) if the binary integer variable is adopted to represent the overhaul state, the cluster unit has large scale and unreasonable overhaul; (2) if the maintenance state is represented by an integer variable, maintenance allocation in the cluster unit cannot be accurately described, and the maintenance continuity constraint of a single unit cannot be met;
The invention provides a medium-scale unit clustering method, which aims to avoid oversized cluster units, and divides a large-scale cluster unit into a plurality of medium-scale cluster units, wherein the same cluster units are adopted in a unit maintenance optimization model and a unit combination optimization model; in the overhaul optimization model, binary integer variables are adopted to represent overhaul states, and the cluster scale should meet overhaul site constraint;
s2, a novel provincial power system unit maintenance optimization model, wherein in the novel provincial power system taking new energy as a main body, the unit maintenance optimization model considers the annual distribution problem of clean energy on one hand and the annual distribution problem of system idle capacity on the other hand; in power supply planning, after new energy and water installation capacity are determined, the optimal allocation capacity of pumped storage is determined by adopting multi-scheme comparison, namely, a plurality of pumped storage capacity schemes are assumed, each scheme meets the system load requirement by increasing and decreasing the thermal power capacity, and each combined scheme is required to have minimum system coal consumption and minimum idle capacity; in order to reduce the solving scale, unit overhaul optimization usually adopts a block load curve, but the method cannot consider the time sequence fluctuation of the load and cannot fully consider the overhaul arrangement of the pumped storage unit;
From a macroscopic view, the overhaul of the thermal power generating unit is arranged as much as possible in the period of large new energy generating capacity, so that the new energy is favorably consumed; arranging water and electricity and pumped storage units to overhaul as much as possible in a period with smaller peak-valley difference of residual load of the system, and meeting the requirement of the system on peak regulation capacity while promoting new energy consumption; the final aim of the strategy is to reduce the coal consumption of the system operation; the invention provides a novel provincial power system unit maintenance optimization model, which takes the minimum idle capacity of a system as an objective function, takes the balance of the power of the system and the balance of peak shaving capacity as constraint conditions, and simultaneously considers constraint conditions such as unit maintenance continuity constraint, maintenance site constraint, maintenance period constraint and the like;
(1) And (3) calculating an objective function of the overhaul optimization model, wherein the minimum idle capacity of the system is adopted as the objective function, and the expression is as follows:
wherein: i is the serial number of the cluster unit; n (N) th 、N h 、N p Respectively the cluster numbers of thermal power, hydroelectric power and pumped storage units in the system;the installed capacities of the thermal power, hydroelectric power and pumped storage cluster unit i are respectively; />The overhaul state of the thermal power, hydroelectric power and pumped storage cluster unit i in the period d is 0-1 integer variable, 1 represents overhaul, and 0 represents non-overhaul; />To calculate the maximum value of the residual load in the period, D t For t period system load demand, D d，max For maximum system load in period d, r is a standby coefficient, N w 、N pv The number of wind power and photovoltaic cluster units is +.>The active power output of the wind power and photovoltaic cluster unit i in the period t is respectively calculated, and d is the overhauling optimization calculation period; y is a calculation period of 8760 hours in the whole year;
(2) And calculating a maintenance optimization model target constraint condition, wherein the calculation is specifically as follows:
a) System power balance constraint:
b) System peak shaving capacity balance constraint:
after large-scale new energy grid connection, the peak-valley difference of the residual load of the system is further increased, if the maintenance arrangement is unreasonable, a large amount of new energy sources can be abandoned, and the coal consumption of the system is increased; the hydropower and pumped storage unit overhaul can be arranged in a period with larger peak-valley difference of residual load of the system through peak regulation capacity balance constraint, and the expression is as follows:
wherein: gamma ray i The average peak regulation depth coefficient of the cluster thermal power generating unit i;respectively calculating the maximum value and the minimum value of the residual load in the period;
c) Other constraints
The overhaul state of the unit is 0, 1 integer constraint, 0 represents a non-overhaul state, 1 represents an overhaul state; maintenance continuity constraint, and continuously stopping during maintenance of each unit; the maintenance site is restricted, and the number of maintenance units in the same period is mainly controlled; the maintenance period is constrained, and the non-maintenance of the hydroelectric generating set in the flood period and the non-maintenance of the heating generating set in the winter heating period are mainly controlled;
S3, a novel provincial power system unit combination optimization model;
(1) The unit combination optimization model function calculation is carried out, the model takes the minimum total cost as an objective function, and the total cost comprises the sum of the thermal power generation cost of the system and the economic losses of wind discarding, light discarding and water discarding;
wherein: f (F) i (. Cndot.) is the operating cost function of the cluster thermal power unit i;active output of the cluster thermal power generating unit i in a period t; />The method comprises the steps of (1) starting cost of a cluster thermal power generating unit i in a period t; />The shutdown cost of the cluster thermal power generating unit i in the period t is calculated; t is the total period number in the power generation planning period, t=24 hours; n (N) th The number of the thermal power generating units is the number of the cluster thermal power generating units in the system; n (N) h 、N w 、N pv The number of the hydropower, wind power and photovoltaic cluster units in the system is respectively; />The method comprises the steps of respectively discarding the water electric quantity, the wind electric quantity and the light electric quantity of a cluster unit i in a period t; k (K) h 、K w 、K pv The cost of the unit waste water electric quantity, the unit waste wind electric quantity and the unit waste light electric quantity are respectively;
(2) And calculating constraint conditions of the unit combination optimization model, wherein the constraint conditions mainly comprise: load balance constraint, unit output upper and lower limit constraint, energy storage constraint, hydropower station electric quantity constraint and reserve capacity constraint;
(3) A cluster of thermal power generating units;
a) A cluster thermal power unit combination state;
b) Output constraint of the cluster unit;
c) Thermal power minimum start-stop constraint;
d) Running cost.
2. The novel provincial power system power supply optimization and sequential production simulation method of claim 1, wherein: the medium-scale unit clustering method in the step S1 can directly couple unit overhaul and unit combination optimization, ensure continuity of single unit overhaul, reduce solving scale, improve solving speed and have higher precision;
taking thermal power as an example, the method for determining the cluster size of the unit is as follows:
in the method, in the process of the invention,the installed capacity of the thermal power cluster unit i; c (C) th The total thermal power installed capacity of the system; />D, the thermal power overhaul time length is d; n is a positive integer, and is limited by maintenance sites, calculation scale and the like; []To round the symbol.
3. The novel provincial power system power supply optimization and sequential production simulation method of claim 1, wherein: and S2, under the condition of given system load, wind power and photovoltaic output upper limit, determining the minimum thermal power capacity required by the system according to given pumped storage capacity, so as to analyze the pumped storage replaceable thermal power capacity, and when the thermal power capacity is smaller than the minimum thermal power capacity required by the system, the maintenance plans of all power supplies cannot meet the system operation requirement in any way through optimization.
4. The novel provincial power system power supply optimization and sequential production simulation method of claim 1, wherein: the specific contents of the constraint condition calculation of the combined optimization model in the step S3 are as follows:
(1) Load balancing constraints
The sum of the output of each power supply period of the power system is equal to the load of the system period, and the expression is as follows:
wherein: d (D) t System load for period t;
(2) Upper and lower limit constraint of unit output
The device is suitable for power supplies such as wind power, photovoltaic, thermal power, hydroelectric power, pumped storage and the like; for wind power and photovoltaic output, the upper limit is the maximum output of the unit under the corresponding wind and light conditions, and the lower limit is 0; for hydropower, the upper limit is the expected force and the lower limit is the minimum technical force; for pumped storage, the upper limit is the installed capacity, and the lower limit is 0; for thermal power, the upper limit is the installed capacity, the lower limit is the minimum technical output, and the expression is as follows:
I i,t P i,min ≤P i,t ≤I i,t P i,max (7)
wherein: i=1, 2, …, N; t=1, 2, …, T;
(3) Energy storage constraint
The energy storage constraint expression is as follows:
wherein: s is S s (t) is the state of charge of the energy storage system during period t;and->Charging and discharging power of the energy storage system in the t period respectively; η (eta) c Representing the energy storage conversion efficiency of the energy storage system, and taking 0.75; s is S max And S is min The upper limit and the lower limit of the energy storage state of the energy storage system are respectively;
(4) Hydropower station electric quantity constraint
Considering the electric quantity constraint determined by the average output of different hydrologics, the expression is as follows:
in the method, in the process of the invention,average output of the clustered hydroelectric generating set;
(5) Spare capacity constraint
The reserve rate of the rotary reserve and the load reserve is 8 percent, and the rotary reserve and the load reserve are jointly born by thermal power, hydropower and pumped storage; the reserve rate of cold standby is 5%, all the reserve rate is borne by the thermal power unit, and the expression is as follows:
wherein:respectively carrying out rotary standby and load standby for t time periods of the cluster thermal power, hydroelectric power and pumped storage i units; />And (5) cold standby for the cluster thermal power i unit t period.
5. The novel provincial power system power supply optimization and sequential production simulation method of claim 1, wherein: the cluster thermal power generating unit in the step S3 is specifically in the following combination state:
group thermal power unit i is composed of J i The similar units are formed, and the constraint expression of the change of the starting capacity between adjacent time periods is as follows:
C i O (t)＝C i O (t-1)+C i U (t)-C i D (t) (11)
wherein: c (C) i O (t-1)、C i O (t) the starting capacity of the cluster thermal power generating unit i at t-1 and t time periods respectively, C i O (t)＝I i (t)·C i ，I i (t)∈{0,J i }，C i Is the average single machine installed capacity of the cluster thermal power generating unit i,I i (t) is a boot integer variable; c (C) i U (t) is the starting capacity of the cluster thermal power generating unit i in the period of t, C i U (t)＝U i (t)·C i ，U i (t)∈{0,J i }；C i D (t) is the shutdown capacity of the cluster thermal power generating unit i in the period of t, C i D (t)＝D i (t)·C i ，D i (t)∈{0,J i }。
The unit state variable constraint conditions are as follows:
0≤I i (t),U i (t),D i (t)≤J i (12)。
6. the novel provincial power system power supply optimization and sequential production simulation method of claim 1, wherein: the output constraint of the cluster unit in the S3 is specifically as follows:
output power P of cluster thermal power generating unit i in period t i (t) satisfying the constraint:
α i (t)·C i O (t)≤P i (t)≤C i O (t) (13)
wherein alpha is i And (t) is the minimum technical output rate of the cluster thermal power generating unit i, and is obtained by weighted average of the minimum technical output rate of a single machine.
7. The novel provincial power system power supply optimization and sequential production simulation method of claim 1, wherein: the thermal power minimum start-stop constraint in the step S3 is specifically as follows:
after the thermal power generating unit is connected with the power grid, the thermal power generating unit can be stopped only after a certain running time is ensured, and after the thermal power generating unit is stopped, the power generating unit can be connected with the power grid only after a certain stopping time is ensured, and the minimum starting and stopping constraint expression of the cluster unit is as follows:
in the formula, MUT i 、MDT i For minimum start-up and shut-down time.
8. The novel provincial power system power supply optimization and sequential production simulation method of claim 1, wherein: the operation cost in the step S3 is specifically as follows:
the operation cost comprises the power generation cost F i P (t) start-up cost F i U (t) and shutdown costs F i D (t); the power generation cost expression is as follows:
F i P (t)＝K i F ·C i O (t)+K i J ·(P i (t)-α i (t)·C i O (t)) (16)
wherein K is i F The running coal consumption is the minimum output of the unit starting capacity technology; k (K) i J A linear power generation coal consumption slope weighted average value of the cluster unit i;
the starting cost and the shutdown cost function of the cluster unit i are as follows:
F i U (t)＝K i U ·C i U (t)， F i D (t)＝K i D ·C i D (t) (17)
in the formula, parameter K i U And K i D The coal consumption corresponding to the unit start-up capacity and the unit stop capacity are respectively indicated.
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