CN113708363A - Scheduling flexibility assessment method and system for electric heating combined system - Google Patents

Scheduling flexibility assessment method and system for electric heating combined system Download PDF

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CN113708363A
CN113708363A CN202010438451.9A CN202010438451A CN113708363A CN 113708363 A CN113708363 A CN 113708363A CN 202010438451 A CN202010438451 A CN 202010438451A CN 113708363 A CN113708363 A CN 113708363A
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scene
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wind power
flexibility
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戴赛
刘勤
罗卫华
兰强
陈艳波
焦洋
刘芳
丁强
张传成
蔡帜
胡晓静
崔晖
韩彬
燕京华
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
Southwest Branch of State Grid Corp
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China Electric Power Research Institute Co Ltd CEPRI
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Abstract

The invention provides a scheduling flexibility evaluation method of an electric heating combined system, which comprises the following steps: determining uncertain scenes of day-ahead wind power and loads based on historical wind power data; the method comprises the steps of substituting prediction data of day-ahead wind power, electric load and heat load in an uncertain scene into a pre-constructed optimized scheduling model to determine a unit combination plan; evaluating the scheduling flexibility of the electric heating combined system by utilizing a pre-constructed safety check model based on a unit combination plan; wherein, the safety check model comprises: taking the minimum of the abandoned wind and the cut load under the uncertain scene as an objective function, introducing variables of the spare insufficient scene frequency and the spare insufficient total amount into the objective function, and obtaining a preset scheduling flexibility evaluation index value according to the variables; the optimized scheduling model comprises the following steps: the method aims to minimize the cost of a thermal power generating unit and a cogeneration unit; the index evaluation system considers the scheduling flexibility of the conventional unit and the cogeneration unit and can reflect the abundance of the system for up-regulation and down-regulation.

Description

Scheduling flexibility assessment method and system for electric heating combined system
Technical Field
The invention belongs to the technical field of electric power system scheduling automation, and particularly relates to a scheduling flexibility evaluation method and system of an electric heating combined system.
Background
With the explosion of energy conversion equipment such as a combined heat and power generation unit, the energy network coupling is increasingly tight. Uncertain variables exist in the electric heat coordination optimization scheduling model, and sources of uncertainty comprise prediction errors of wind power output and prediction errors of electric and heat load power. When the predicted value of the wind power output is higher than the actual value or the predicted value of the electricity and heat load is lower than the actual value, the output of the controllable energy supply equipment needs to be increased or part of load needs to be cut off to ensure electric power balance; on the contrary, when the predicted value of the wind power output is lower than the actual value or the predicted value of the electricity and heat load is higher than the actual value, the output of the controllable energy supply equipment needs to be reduced or part of renewable energy needs to be wasted, which brings the risk of increasing the scheduling cost for the electric-heat combined system. The traditional 'heating and power' operation mode leads to a mandatory relationship between heat and electricity; and the thermoelectric unit participates in the mode of peak regulation according to the actual condition, lacks optimization overall planning flexibility, has restricted the effect that the thermoelectric unit participates in the peak regulation.
Disclosure of Invention
Aiming at the existing traditional operation mode of 'fixing electricity by heat', the method leads to a mandatory relationship between heat and electricity; the invention provides a scheduling flexibility evaluation method of an electric heating combined system, which comprises the following steps of:
determining uncertain scenes of day-ahead wind power and loads based on historical wind power data;
bringing the day-ahead wind power, electric load and heat load prediction data under the uncertain scene into a pre-constructed optimized scheduling model to determine a unit combination plan;
evaluating the scheduling flexibility of the electric heating combined system by utilizing a pre-constructed safety check model based on the unit combination plan;
wherein the security check model comprises: taking the minimum of abandoned wind and cut load under an uncertain scene as an objective function, introducing variables of the number of times of the standby insufficient scene and the total standby insufficient amount into the objective function, and obtaining a preset scheduling flexibility evaluation index value according to the variables;
the optimized scheduling model comprises: the aim is to minimize the cost of the thermal power generating unit and the cogeneration unit.
Preferably, the construction of the optimized scheduling model includes:
determining a unit combination in front of the day according to the wind power, the electric load and the heat load prediction data in front of the day under the uncertain scene, and constructing an objective function by taking the minimum start-stop cost and the minimum operation cost of the thermal power unit and the minimum start-stop cost and the minimum operation cost of the cogeneration unit as targets;
the method is characterized in that the constraint conditions are CON unit power balance constraint, CON unit output upper and lower limit constraint, CON unit climbing constraint, CON unit transmission line capacity constraint, CHP unit power output constraint, CHP unit heat output constraint, thermodynamic system heating station constraint, thermodynamic system heat supply network constraint, thermodynamic system heat exchange station constraint and heat load constraint.
Preferably, the building of the security check model includes:
setting a target function by taking the minimum wind curtailment and load shedding amount of the power system under an uncertain scene as a target;
meanwhile, the constraint conditions are the power balance constraint of the CON unit, the upper and lower limit constraint of the output of the CON unit, the climbing constraint of the CON unit, the capacity limit constraint of a power transmission line of the CON unit, the power output constraint of the CHP unit, the heat output constraint of the CHP unit, the heating station constraint of the thermodynamic system, the heat supply network constraint of the thermodynamic system, the heat exchange station constraint of the thermodynamic system and the heat load constraint;
introducing iteration variables and iteration times of the spare shortage scene times and the spare shortage total amount into the objective function;
based on the iteration variables and the iteration times of the spare shortage scene times and the spare shortage total amount, calculating an upper spare shortage total amount, an upper spare shortage scene times, a lower spare shortage total amount and a lower spare shortage scene occurrence time after iteration respectively when the upper spare shortage of the system and the lower spare shortage of the system exist;
and obtaining an index value corresponding to each scheduling flexibility evaluation index based on a preset scheduling flexibility evaluation index and the number of times of the insufficient up-regulation standby total amount, the insufficient up-regulation standby scene number, the insufficient down-regulation standby total amount, the insufficient down-regulation standby scene occurrence number and the iteration number after the iteration.
Preferably, the scheduling flexibility evaluation indicator includes: an up-regulation flexibility deficiency probability index, an up-regulation flexibility deficiency expectation index, a down-regulation flexibility deficiency probability index, and a down-regulation flexibility deficiency expectation index.
Preferably, the number of occurrences of the reduced standby deficiency scenario and the total reduced standby quantity are calculated as follows:
Figure BDA0002501869730000021
in the formula, deltadownFor reducing the number of occurrences of a backup deficiency scenario, ηdownIn order to down-regulate the amount of insufficient reserve,
Figure BDA0002501869730000033
is the wind curtailment in the s-th iteration, T is the total time period, T is the time period, NwIs the total number of the wind power plant, and w is the wind power plant;
the number of times of the up standby shortage scene and the up standby shortage total amount are calculated as follows:
Figure BDA0002501869730000031
in the formula, deltaupFor upscaling with insufficient scene times, etaupIn order to adjust up the amount of the shortage,
Figure BDA0002501869730000034
is the load shedding at the s-th iteration, NdIs the total number of the load nodes, d is the load node;
the probability index with insufficient up-regulation flexibility, the expected index with insufficient up-regulation flexibility, the probability index with insufficient down-regulation flexibility and the expected index with insufficient down-regulation flexibility are respectively calculated according to the following formulas:
Figure BDA0002501869730000032
in the formula, PUFNS，tTo adjust up the probability index of insufficient flexibility, EUFNS，tTo adjust up the flexibility is not sufficiently desired, PDFNS，tTo adjust the probability of lack of flexibility down, EDFNS，tInsufficient flexibility is desired for turndown.
Preferably, the determining an uncertain scene of the day-ahead wind power and the load based on the historical wind power data includes:
the method comprises the following steps of (1) carrying out interval division on wind power output of an electric power system;
acquiring historical wind power data of each interval, and obtaining historical prediction error distribution of wind power output based on the historical wind power data of each interval;
determining a covariance matrix of a dynamic scene in the day ahead based on the historical prediction error distribution, and generating a plurality of uncertain scenes by using MATLAB based on the covariance matrix;
wherein the historical wind power data comprises: predicted and actual data of wind power and load.
Preferably, the determining a covariance matrix of a dynamic scene in the day ahead based on the historical prediction error distribution, and generating a plurality of uncertain scenes by using MATLAB based on the covariance matrix includes:
calculating the covariance of any two multivariate normal random vectors in different time periods by adopting an exponential function method based on the historical prediction error distribution, and constructing a covariance matrix by the covariance to determine a day-ahead dynamic scene;
obtaining a sample of a multivariate normal random vector by adopting a mathematical algorithm based on a day-ahead dynamic scene determined by the covariance matrix;
based on the historical prediction error distribution, fitting by adopting an accumulative empirical probability distribution function based on a pre-obtained wind power prediction value to obtain a relative prediction error;
calculating to obtain an error scene based on the multivariate normal random vector sample and the relative prediction error;
and calculating to obtain an uncertain scene by adopting a cumulative probability distribution function of standard normal distribution based on the multivariate normal random vector sample and the error scene.
Preferably, the cumulative empirical probability distribution function is calculated as follows:
Figure BDA0002501869730000041
Figure BDA0002501869730000042
in the formula, FlTheta is a wind power random variable e and a sample delta for an accumulated empirical probability distribution function of a prediction errorkK is the number of historical wind power prediction data of each interval, deltakForecasting data for the interval historical wind power;
the uncertain scene is calculated as follows:
Φ(Zt)＝Fl(Δwt)
Figure BDA0002501869730000043
in the formula, phi (-) is an uncertain scene obtained by cumulative calculation of a cumulative probability distribution function of a standard normal distribution, ZtFor multivariate normal random vector samples, Δ wtIs an error scenario.
Based on the same conception, the invention provides a scheduling flexibility evaluation system of an electric heating combined system, which comprises the following steps: the system comprises a scene module, a unit combination module and an evaluation module;
the scene module is used for determining uncertain scenes of day-ahead wind power and loads based on historical wind power data;
the unit combination module is used for substituting the day-ahead wind power, electric load and thermal load prediction data in the uncertain scene into a pre-constructed optimized scheduling model to determine a unit combination plan;
the evaluation module is used for evaluating the scheduling flexibility of the electric heating combined system by utilizing a pre-constructed safety check model based on the unit combination plan;
wherein the security check model comprises: taking the minimum of abandoned wind and cut load under an uncertain scene as an objective function, introducing variables of the number of times of the standby insufficient scene and the total standby insufficient amount into the objective function, and obtaining a preset scheduling flexibility evaluation index value according to the variables;
the optimized scheduling model comprises: the aim is to minimize the cost of the thermal power generating unit and the cogeneration unit.
Preferably, the unit combination module includes: an optimized scheduling model target submodule and an optimized scheduling model constraint submodule;
the optimized scheduling model target submodule is used for determining a unit combination in front of the day according to the wind power, the electric load and the heat load prediction data in the day under the uncertain scene, and constructing a target function by taking the minimum start-up and shut-down cost and the minimum operation cost of the thermal power unit and the minimum start-up and shut-down cost and the minimum operation cost of the cogeneration unit as targets;
and the optimized scheduling model constraint submodule is used for using the power balance constraint of the CON unit, the upper and lower limit constraint of the output of the CON unit, the climbing constraint of the CON unit, the capacity limit constraint of a power transmission line of the CON unit, the electrical output constraint of the CHP unit, the heat output constraint of the CHP unit, the heating station constraint of the thermodynamic system, the heat network constraint of the thermodynamic system, the heat exchange station constraint of the thermodynamic system and the heat load constraint as constraint conditions.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention provides a scheduling flexibility evaluation method of an electric heating combined system, which comprises the following steps: determining uncertain scenes of day-ahead wind power and loads based on historical wind power data; bringing the day-ahead wind power, electric load and heat load prediction data under the uncertain scene into a pre-constructed optimized scheduling model to determine a unit combination plan; evaluating the scheduling flexibility of the electric heating combined system by utilizing a pre-constructed safety check model based on the unit combination plan; wherein the security check model comprises: taking the minimum of abandoned wind and cut load under an uncertain scene as an objective function, introducing variables of the number of times of the standby insufficient scene and the total standby insufficient amount into the objective function, and obtaining a preset scheduling flexibility evaluation index value according to the variables; the optimized scheduling model comprises: the method aims to minimize the cost of a thermal power generating unit and a cogeneration unit; the index evaluation system considers the scheduling flexibility of the conventional unit and the cogeneration unit and can respectively reflect the abundance of the system for up-regulation and down-regulation.
Drawings
FIG. 1 is a flow chart of a method provided by the present invention;
FIG. 2 is an evaluation flowchart of a scheduling flexibility evaluation method for an electric-thermal combined system according to an embodiment of the present invention;
FIG. 3 is a flowchart of a system flexibility evaluation based on dynamic scene generation according to an embodiment of the present invention;
FIG. 4 is a system diagram of an IEEE-24 node provided by an embodiment of the present invention;
FIG. 5 is a diagram of a 16-node thermodynamic system provided by an embodiment of the present invention;
FIG. 6 is a diagram of predicted data of wind power, load, and outdoor temperature provided by an embodiment of the present invention;
FIG. 7 is an uncertain scene graph of wind power provided by an embodiment of the present invention;
FIG. 8 is an uncertain scene graph of a load according to an embodiment of the present invention;
fig. 9 is a system configuration diagram according to an embodiment of the present invention.
Detailed Description
The embodiments of the present invention will be further explained with reference to the drawings.
Example 1:
the invention provides a scheduling flexibility evaluation method of an electric heating combined system, which comprises the steps of firstly, considering the uncertainty characteristics of wind power and the coordination and cooperation of a conventional unit and a cogeneration unit, defining a system operation flexibility index system, considering the upward flexibility and the downward flexibility of the conventional unit and the cogeneration unit, and respectively reflecting the abundance of system up-regulation standby and down-regulation standby. Then, based on a dynamic scene generation algorithm, a scheduling flexibility evaluation method of the electric heating combined system is provided, the flexibility of the system is evaluated according to a system scheduling result, and the method is introduced by combining with a method flow chart of fig. 1, and specifically comprises the following steps:
step 1: determining uncertain scenes of day-ahead wind power and loads based on historical wind power data;
step 2: bringing the day-ahead wind power, electric load and heat load prediction data under the uncertain scene into a pre-constructed optimized scheduling model to determine a unit combination plan;
and step 3: evaluating the scheduling flexibility of the electric heating combined system by utilizing a pre-constructed safety check model based on the unit combination plan;
wherein, the step 1: the method for determining the uncertain scene of the day-ahead wind power and the load based on the historical wind power data specifically comprises the following steps:
defining a flexibility index system for the output coordination capacity of the conventional unit and the wind power plant in the operation stage;
the parameter PUFNS refers to the probability that the demand cannot be met when the unit is adjusted up in a running day.
Figure BDA0002501869730000061
In the formula: pUFNS，tTo adjust up the insufficient flexibility probability, RUtThe capacity is adjusted up for the system to be available at time t; pnet，t+1And Pnet，tNet load amounts at times t and t +1, respectively;
Figure BDA0002501869730000062
and Pi，tRespectively setting the upper output limit of the unit i and the actual output at the moment t; URiThe climbing rate of the unit i is obtained; Δ t is schedulingSpacing; pr {. denotes the probability; n is a radical ofGThe total number of the generators;
an expected up flexibility not supported (expected up flexibility not supported) parameter EUFNS means that a unit can provide an expected value of the difference between the up-regulated standby and the actual demand in the operation day.
Figure BDA0002501869730000071
In the formula, EUFNS，tFor insufficient flexibility of up-regulation, Δ RUtThe difference between the up-regulation reserve and the actual demand can be provided for the unit;
the downward regulation of the flexibility-insufficient probability (probability of downward flexibility not supported) parameter PDFNS refers to the probability that the unit is standby and cannot meet the requirement in the operating day.
Figure BDA0002501869730000072
In the formula: pDFNS，tTo adjust the probability of insufficient flexibility, RDtDown-regulation capacity available to the system at time t; DR (digital radiography)iIs its downhill rate;
the expected (expected down flexibility not supported) parameter EDFNS means that the unit can provide an expected value of the difference between the lower reserve and the actual demand in the operation day.
Figure BDA0002501869730000073
In the formula, EDFNS，tInsufficient flexibility is desired for turndown.
A dynamic scene generation method is provided, which generates a scene considering random variable correlation based on a large amount of historical data. The system flexibility evaluation flow chart based on dynamic scene generation in conjunction with fig. 3 is described as follows:
and generating a wind power prediction box in each output interval. The wind power output interval is divided according to historical wind power prediction data (historical wind power and load prediction data), the historical prediction data obtained through statistics and actual output are put into prediction boxes according to the size of a predicted value, and the distribution of relative prediction errors of the prediction boxes is obtained through calculation.
Determining a covariance matrix sigma of a day ahead dynamic scene. The covariance matrix Σ can be expressed as:
Figure BDA0002501869730000081
in the formula, σm，nThe covariance of the random variables in the m period and the n period is the correlation between the two random variables.
The covariance is calculated using an exponential function:
Figure BDA0002501869730000082
where epsilon is used to determine the correlation of the random variables for the m, n periods.
After the covariance matrix is determined, S sample random vectors Z of multivariate normal random vectors obeying N (0, sigma) are generated by using a MATLAB mathematical algorithm.
And fitting the accumulated empirical probability distribution function of the relative prediction error of the data in each prediction box. For the 1 st prediction box, K data in the prediction box are arranged from small to large as delta1，δ2，...，δK. The cumulative empirical probability distribution function of the wind power prediction error e is:
Figure BDA0002501869730000083
Figure BDA0002501869730000084
for multivariate normal random vector sampleAnd (4) performing inverse row transformation. Converting S random vectors Z into S error scenes with correlation delta w by using an equal probability inverse transformation formula shown in formulas (5) to (6)tAnd generating S wind power scenes based on the predicted wind power in the day ahead.
Φ(Zt)＝Fl(Δwt) (5)
Figure BDA0002501869730000085
Where Φ () is the cumulative probability distribution function of a standard normal distribution.
And calculating a flexibility index based on the generated uncertain scene.
Step 2: bringing the day-ahead wind power, electric load and heat load prediction data under the uncertain scene into a pre-constructed optimized scheduling model to determine a unit combination plan, which specifically comprises the following steps:
determining the unit combination of the day according to the day-ahead wind power, electric load and heat load prediction data to obtain the starting and stopping states of the conventional unit and the cogeneration unit at the time t as Ui，t(variable 0-1), the number of simulations S is set to 0, and the total number of iterations is set to S. A model of the day-ahead crew combination is established as follows.
And establishing a model of the day-ahead unit combination, and constructing an optimal scheduling model of the coordination of the power system and the centralized heating network by taking the optimal economy (the lowest cost) as an objective function.
The objective function comprises the start-up and shut-down cost and the operation cost of the thermal power generating unit, the start-up and shut-down cost and the operation cost of the cogeneration unit and the wind abandoning and load shedding punishment of the system; and the thermal power generating unit, the CHP unit, the abandoned wind and the load shedding penalty are weighted by the probability of the cost under each scene. The mathematical form of the objective function is specifically shown as follows:
Figure BDA0002501869730000091
in the formula (I), the compound is shown in the specification,
Figure BDA0002501869730000092
representing the startup cost of a Condensation (CON) unit;
Figure BDA0002501869730000093
representing the shutdown cost of the CON unit;
Figure BDA0002501869730000094
representing the starting cost of the CHP unit;
Figure BDA0002501869730000095
represents the shutdown cost of the CHP unit;
Figure BDA0002501869730000096
representing the running cost of the CON unit;
Figure BDA0002501869730000097
represents the operation cost of the CHP unit; sigmaDPenalizing a cost coefficient for load shedding;
Figure BDA0002501869730000098
respectively representing the output of the CON unit and the output of the CHP unit.
The basic constraints of the power system comprise power balance constraints (8), unit output upper and lower limit constraints (9), wind power plant output constraints (10), unit minimum start-up and shut-down time constraints (11) - (12), unit start-up and shut-down cost constraints (13) - (14), unit climbing constraints (15) - (16), unit flexible adjustment constraints (17) and transmission line capacity constraint constraints (18) - (19).
Figure BDA0002501869730000099
Figure BDA00025018697300000910
Figure BDA00025018697300000915
Figure BDA00025018697300000911
Figure BDA00025018697300000912
Figure BDA00025018697300000913
Figure BDA00025018697300000914
Figure BDA0002501869730000101
Figure BDA0002501869730000102
Figure BDA0002501869730000103
PLl，t＝(θn，t-θo，t)/xl，θref，t＝0 (18)
Figure BDA0002501869730000104
In the formula, PLl，tIs the transmission power of the transmission line; PD (photo diode)d，tRepresenting the load capacity of the load node;
Figure BDA0002501869730000105
representing a wind power predicted value; PW (pseudo wire)w，tRepresenting wind power output; g (n) represents a bitA set of CON units at n nodes, c (n) represents a set of CHP units at n nodes, w (n) represents a set of wind farms at n nodes, l (n) represents a set of transmission lines connected to n nodes, d (n) represents a set of load users at n nodes;
Figure BDA0002501869730000106
representing the starting and stopping state of the CON unit;
Figure BDA0002501869730000107
the lower limit and the upper limit of the unit output are set;
Figure BDA0002501869730000108
Figure BDA0002501869730000109
representing the time that the unit has been continuously started and stopped; t ison，i、Toff，iRepresenting unit startup and shutdown time constraints; suiAnd sdiRepresenting the unit start-up and shut-down costs of the unit. URiAnd DRiThe unit is restrained from climbing up and down; thetan，tAnd thetao，tIs the phase angle, theta, of the node connected to line lref，tTo balance the phase angle of the node, xlIs the reactance of line l;
Figure BDA00025018697300001014
is the maximum transmitted power capacity of the line.
The constraint conditions only give the operation constraint conditions of the CON unit, and the electrical output and the thermal output of the CHP unit are as the formula (20). The operating cost of the CHP unit is as shown in formula (21). Other constraints of the CHP unit are the same as those of the CON unit, and detailed formulas are not given.
Figure BDA00025018697300001010
Figure BDA00025018697300001011
In the formula (I), the compound is shown in the specification,
Figure BDA00025018697300001012
the electric output power and the heat output power of the jth CHP unit at the moment t are respectively;
Figure BDA00025018697300001013
respectively representing the electric output power and the heat output power of the jth cogeneration unit corresponding to the kth extreme point in the feasible region;
Figure BDA0002501869730000111
the output coefficient of the kth extreme point of the jth CHP unit at the time t is represented; NKjThe number of extreme points in the feasible region of the jth cogeneration unit; cchp(. is the operating cost of the cogeneration unit;
Figure BDA0002501869730000112
representing the operating cost of each extreme point.
The constraints of the thermodynamic system include heating station constraints (22) - (23), heat grid constraints (24) - (28), heat exchange stations and heat load constraints (29) - (31).
Figure BDA0002501869730000113
Figure BDA0002501869730000114
Figure BDA0002501869730000115
Figure BDA0002501869730000116
Figure BDA0002501869730000117
Figure BDA0002501869730000118
Figure BDA0002501869730000119
Figure BDA00025018697300001110
Figure BDA00025018697300001111
Figure BDA00025018697300001112
In the formula, cwRepresents the specific heat capacity of the fluid in the pipeline; mc ofg，tRepresenting a mass flow rate of the heat exchange station;
Figure BDA00025018697300001113
represents the heating temperature of the heat exchange station;
Figure BDA00025018697300001114
representing the regenerative temperature of the heat exchange station; sgIndicating the heat exchange station number.
Figure BDA00025018697300001115
And
Figure BDA00025018697300001116
respectively representing the lower limit and the upper limit of the heating temperature of the heating plant.
Figure BDA00025018697300001117
Represents the outlet temperature of the water supply pipe p at time t;
Figure BDA00025018697300001118
represents the inlet temperature of the water supply pipe p at time t;
Figure BDA00025018697300001119
represents the outdoor temperature; mu.sp，Lp，RpRespectively representing the heat loss coefficient, the length and the radius of the pipeline;
Figure BDA00025018697300001120
representing the mass flow rate of fluid in the water supply pipeline; rhowRepresenting the density of the fluid; Δ t represents a scheduling time interval.
Figure BDA00025018697300001121
Representing the outlet temperature of the water return pipe;
Figure BDA0002501869730000121
representing the mass flow rate of the fluid in the return pipe;
Figure BDA0002501869730000122
and
Figure BDA0002501869730000123
the temperature at the node m of the water supply net and the water return net is represented; omegapipe-And Ωpipe+Respectively representing the pipe with the node m as the end point and the starting point.
Figure BDA0002501869730000124
Represents the thermal load; mhh，t，
Figure BDA0002501869730000125
The hot water flow, the water supply temperature and the water return temperature at the inlet of the heat exchange station are shown.
Figure BDA0002501869730000126
Figure BDA0002501869730000127
Respectively the lower limit and the upper limit of the return water temperature.
Figure BDA0002501869730000128
Represents the indoor temperature of the building;
Figure BDA0002501869730000129
represents the outdoor temperature; chi shapeh，tIs the heat transfer coefficient per unit temperature difference.
And step 3: evaluating the scheduling flexibility of the electric heating combined system by utilizing a pre-constructed safety check model based on the unit combination plan, and specifically comprising the following steps of:
generating a wind power and load time sequence curve by adopting a dynamic scene generation method according to the historical prediction error distribution of the wind power and the electrical load, setting abandoned wind and load shedding variables based on a unit start-stop plan, and solving the following safety check model:
the minimum wind curtailment and load shedding penalty under an uncertain scene are taken as targets. The objective function is:
Figure BDA00025018697300001210
in the formula:
Figure BDA00025018697300001211
and
Figure BDA00025018697300001212
respectively the wind curtailment and the load shedding under the scene s, cWAnd cDRespectively punishment for wind abandon and load shedding.
The constraint conditions include: basic constraints of the power system comprise a power balance constraint (33), a unit output upper and lower limit constraint (34), unit climbing constraints (35) - (36) and transmission line capacity limit constraints (37) - (38). The variables in the following constraints have the same meanings as in the formulae (8) to (19), and are not explained again.
Figure BDA00025018697300001213
Figure BDA00025018697300001214
Figure BDA00025018697300001215
Figure BDA00025018697300001216
PLl，t，s＝(θn，t，s-θo，t，s)/xl，θref，t＝0 (37)
Figure BDA00025018697300001217
In the formula (I), the compound is shown in the specification,
Figure BDA0002501869730000131
for solving the ON/OFF state of the CON unit in the above scheduling model
Figure BDA0002501869730000132
Obtaining a start-stop plan;
Figure BDA0002501869730000133
representing the output of a conventional unit under a scene s; PLl，t，sThe transmission power of the transmission line under the uncertain scene s is obtained;
Figure BDA0002501869730000134
representing the load under an uncertain scene s;
Figure BDA0002501869730000135
and representing the wind power output under the scene s.
The operation constraint conditions of the CON unit (pure condensing thermal power unit) are only given in the constraint conditions, and the electrical output and the thermal output of the CHP unit are as the formula (39). Other constraints of the CHP plant (cogeneration plant) are the same as those of the CON plant, and a detailed formula is not given.
Figure BDA0002501869730000136
In the formula (I), the compound is shown in the specification,
Figure BDA0002501869730000137
the CHP unit start-stop plan in the scheduling model is obtained through solving;
Figure BDA0002501869730000138
respectively representing the electric output power and the heat output power of the jth CHP unit at the moment t under the scene s;
Figure BDA0002501869730000139
Figure BDA00025018697300001310
respectively setting the electric output power and the heat output power of the jth cogeneration unit in the feasible region corresponding to the kth extreme point under the scene s;
Figure BDA00025018697300001311
representing the output coefficient of the kth extreme point of the jth CHP unit at the moment t under the scene s; NKjThe number of extreme points in the feasible region of the jth cogeneration unit.
The constraints of the thermodynamic system include heating station constraints (40) - (41), heat grid constraints (42) - (46), heat exchange stations and heat load constraints (47) - (49).
Figure BDA00025018697300001312
Figure BDA00025018697300001313
Figure BDA00025018697300001314
Figure BDA00025018697300001315
Figure BDA00025018697300001316
Figure BDA0002501869730000141
Figure BDA0002501869730000142
Figure BDA0002501869730000143
Figure BDA0002501869730000144
Figure BDA0002501869730000145
In the formula, subscript s represents an uncertain scene;
Figure BDA0002501869730000146
represents the heating temperature of the heat exchange station;
Figure BDA0002501869730000147
representing the regenerative temperature of the heat exchange station;
Figure BDA0002501869730000148
indicating the outlet temperature of the water supply pipe p at time t；
Figure BDA0002501869730000149
Represents the inlet temperature of the water supply pipe p at time t;
Figure BDA00025018697300001410
representing the outlet temperature of the water return pipe;
Figure BDA00025018697300001411
and
Figure BDA00025018697300001412
the temperature at the node m of the water supply net and the water return net is represented;
Figure BDA00025018697300001413
and the water supply temperature and the water return temperature of the heat exchange station are shown.
Setting 4 variables deltaup，δdown，ηup，ηdown(all initial values are 0) for recording simulation results, and determining the variable Δ W according to the calculation resultsw，t，sIf the number of the wind curtailed wind is not 0, the wind curtailment exists in the s-th iteration, and the downward standby of the system is insufficient, the calculation is carried out according to the following formula:
Figure BDA00025018697300001414
if the variable Δ Dd，t，sIf not all are 0, then the load is cut in the s-th iteration, and the system is insufficiently adjusted up, then the following formula is used for calculation:
Figure BDA00025018697300001415
and setting S as iteration times, stopping iteration until the iteration times are the set total times S, if so, ending the simulation process, and outputting a system flexibility index, wherein the flexibility index is shown as the following formula.
Figure BDA00025018697300001416
In the formula: deltaupUp-modulation with insufficient scene number, deltadownFor reducing the number of occurrences of a backup deficiency scenario, ηupFor up-regulation of the amount of deficiency, etadownTo down-regulate the insufficient reserve total; n is a radical ofwThe total number of the wind power plants; n is a radical ofdThe total number of the load nodes is; t is the total time period.
In summary, the overall flow of the scheduling flexibility evaluation method for the electric heating combined system is shown in fig. 2.
For a better understanding of the present invention and to show the advantages thereof over the prior art, reference is made to the accompanying drawings, which form a part hereof, and in which is shown by way of illustration specific embodiments.
Based on the improved IEEE-24 node power system and the 16 node power system, the effectiveness of the electric heat coordination random optimization scheduling model provided by the invention is verified, and the constructed system is shown in figures 4 and 5. The improved IEEE-24 node system comprises 10 generators, and the improvement point is that 4 CON units are replaced by CHP units, and 6 CON units and 1 wind power station are additionally included. The thermodynamic system comprises 16 nodes and 14 heat transmission pipelines. The predicted data of wind power, electric load, and outdoor temperature are shown in fig. 6. In the calculation example, the cost of the waste wind is set as 100$/MWh, and the cost of the lost load is 600 $/MWh. The proposed electric heat coordination random optimization scheduling model calls YALMIP and Gurobi-8.0.1 on MATLAB 2017b for realization.
The flexibility evaluation model proposed in this section first needs to generate a random wind power and load scene by using a dynamic scene generation method. The dynamic scene generation method needs to establish an accumulated empirical probability distribution function through historical data, the historical data of wind power and load are from data of 9 months in 2017 to 8 months in 2018 provided by Elia of a power transmission operator in Belgium, and the historical wind power and load data are adjusted in proportion.
1000 wind power and load scenes are randomly generated by using a dynamic scene generation method, as shown in fig. 7 and 8.
This subsection performs flexibility assessment on the combined electric and heat system that takes into account the heating of the system. In an IEEE-24 node system, a number 1-6 unit is a conventional thermal power unit, and a number 7-10 unit is a cogeneration unit. Firstly, a unit combination decision and the electric output and the thermal output of the unit under a prediction scene are obtained.
After a unit combination plan under an expected scene is obtained, four indexes for evaluating the flexibility of the system defined in the section are evaluated according to the generated dynamic scene: insufficient up-regulation flexibility rate, insufficient down-regulation flexibility rate, insufficient up-regulation flexibility expectation and insufficient down-regulation flexibility expectation.
Table 1 gives the system operating cost and system flexibility evaluation index values.
TABLE 1 evaluation index for operating cost and flexibility of the System
Figure BDA0002501869730000151
As can be seen from the table, the flexibility and shortage rate of the down regulation of the system is higher in the flexibility evaluation of the electric heating system. That is, when the actual wind power output is higher than the predicted output or the actual load demand is lower than the predicted load, the processing of the unit cannot be reduced, resulting in a condition of wind abandon. Different from the power system analyzed in the previous section, the down regulation flexibility of the electric-heat combined system is poor. Through the analysis, because the combined heat and power generation unit needs to supply heat in winter, the operation domain influence of the combined heat and power generation unit, the electric output range of the combined heat and power generation unit is restrained, and when the wind power output is higher than the predicted output, the combined heat and power generation unit can not reduce the electric output, thereby leading to abandoning the wind.
According to the method presented in this section, the flexibility of the units in the system is then evaluated. The sum of the up/down flexible adjustment power provided by each unit over the entire scheduling period is given, as shown in table 2.
TABLE 2 Flexible Power (MW) provided by each unit over the entire scheduling period
Machine set Up-regulation of flexible power Down-regulated flexible power
1 0 0
2 0 0
3 196.1716 7.989474
4 23.0625 1708.556
5 40.94228 1858.622
6 263.4084 207.6948
7 1570.19 18.77087
8 1626.938 78.06511
9 623.3696 99.31938
10 1402.116 49.42499
Table 2 gives the sum of the up/down flexible adjustment power of all units over the entire scheduling period. As can be seen from table 2, the 4 and 5 units are primarily responsible for the down-regulation of the system. The flexibility of up-regulation of the cogeneration unit is high, and the flexibility of down-regulation is insufficient. As can be seen from the table, the average down-regulation adjusting power of the No. 7 unit is the lowest, and the No. 7 unit can be considered as a key unit influencing the system peak regulation.
Example 2:
based on the same concept, the invention provides a scheduling flexibility evaluation system of an electric heating combined system, which is introduced by combining with a system structure diagram of fig. 9, and specifically comprises the following steps: the system comprises a scene module, a unit combination module and an evaluation module;
the scene module is used for determining uncertain scenes of day-ahead wind power and loads based on historical wind power data;
the unit combination module is used for substituting the day-ahead wind power, electric load and thermal load prediction data in the uncertain scene into a pre-constructed optimized scheduling model to determine a unit combination plan;
the evaluation module is used for evaluating the scheduling flexibility of the electric heating combined system by utilizing a pre-constructed safety check model based on the unit combination plan;
wherein the security check model comprises: taking the minimum of abandoned wind and cut load under an uncertain scene as an objective function, introducing variables of the number of times of the standby insufficient scene and the total standby insufficient amount into the objective function, and obtaining a preset scheduling flexibility evaluation index value according to the variables;
the optimized scheduling model comprises: the aim is to minimize the cost of the thermal power generating unit and the cogeneration unit.
The unit combination module includes: an optimized scheduling model target submodule and an optimized scheduling model constraint submodule;
the optimized scheduling model target submodule is used for determining a unit combination in front of the day according to the wind power, the electric load and the heat load prediction data in the day under the uncertain scene, and constructing a target function by taking the minimum start-up and shut-down cost and the minimum operation cost of the thermal power unit and the minimum start-up and shut-down cost and the minimum operation cost of the cogeneration unit as targets;
and the optimized scheduling model constraint submodule is used for using the power balance constraint of the CON unit, the upper and lower limit constraint of the output of the CON unit, the climbing constraint of the CON unit, the capacity limit constraint of a power transmission line of the CON unit, the electrical output constraint of the CHP unit, the heat output constraint of the CHP unit, the heating station constraint of the thermodynamic system, the heat network constraint of the thermodynamic system, the heat exchange station constraint of the thermodynamic system and the heat load constraint as constraint conditions.
The evaluation module comprises: the safety check model comprises a safety check model target submodule, a safety check model constraint submodule, a safety check model variable submodule, an iteration variable submodule and an index value submodule;
the safety check model target submodule is used for setting a target function by taking the minimum wind curtailment and load shedding amount of the power system under an uncertain scene as a target;
the safety check model constraint submodule is used for simultaneously using CON unit power balance constraint, CON unit output upper and lower limit constraint, CON unit climbing constraint, CON unit transmission line capacity constraint, CHP unit electric output constraint, CHP unit heat output constraint, thermodynamic system heating station constraint, thermodynamic system heat network constraint, thermodynamic system heat exchange station constraint and heat load constraint as constraint conditions;
the safety check model variable submodule is used for introducing the spare shortage scene times, the iteration variables of the spare shortage total amount and the iteration times into the objective function;
the iteration variable submodule is used for calculating an up-regulation reserve shortage total amount, an up-regulation reserve shortage scene frequency, a down-regulation reserve shortage total amount and a down-regulation reserve shortage scene frequency after iteration respectively when the up-regulation reserve of the system is insufficient and the down-regulation reserve of the system is insufficient based on the number of times of the reserve shortage scene, the iteration variable of the reserve shortage total amount and the number of times of iteration;
and the index value submodule is used for obtaining index values corresponding to the scheduling flexibility evaluation indexes on the basis of preset scheduling flexibility evaluation indexes and the iterative times of the insufficient amount of the up-regulation standby, the insufficient scene times of the down-regulation standby and the insufficient scene times of the down-regulation.
The scene module comprises: dividing a submodule, an error distribution submodule and a generation submodule;
the division submodule is used for performing interval division on the wind power output of the power system;
the error distribution submodule is used for acquiring historical wind power data of each interval and obtaining historical prediction error distribution of wind power output based on the historical wind power data of each interval;
the generation submodule is used for determining a covariance matrix of a dynamic scene in the day ahead based on the historical prediction error distribution and generating a plurality of uncertain scenes by using MATLAB based on the covariance matrix;
wherein the historical wind power data comprises: predicted and actual data of wind power and load.
The generation submodule includes: the system comprises a day-ahead dynamic scene unit, a sample unit, a relative prediction error unit, an error scene unit and an uncertain scene unit;
the day-ahead dynamic scene unit is used for calculating the covariance of any two multivariate normal random vectors in different time periods by adopting an exponential function method based on the historical prediction error distribution, and constructing a covariance matrix by the covariance to determine a day-ahead dynamic scene;
the sample unit is used for obtaining a sample of a multivariate normal random vector by adopting a mathematical algorithm based on a day-ahead dynamic scene determined by the covariance matrix;
the relative prediction error unit is used for fitting based on the historical prediction error distribution and the wind power prediction value acquired in advance by adopting an accumulated empirical probability distribution function to obtain a relative prediction error;
the error scene unit is used for calculating an error scene based on the multivariate normal random vector sample and the relative prediction error;
and the uncertain scene unit is used for calculating an uncertain scene by adopting a cumulative probability distribution function of standard normal distribution based on the multivariate normal random vector sample and the error scene.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.

Claims (10)

1. A scheduling flexibility assessment method of an electric heating combined system is characterized by comprising the following steps:
determining uncertain scenes of day-ahead wind power and loads based on historical wind power data;
bringing the day-ahead wind power, electric load and heat load prediction data under the uncertain scene into a pre-constructed optimized scheduling model to determine a unit combination plan;
evaluating the scheduling flexibility of the electric heating combined system by utilizing a pre-constructed safety check model based on the unit combination plan;
wherein the security check model comprises: taking the minimum of abandoned wind and cut load under an uncertain scene as an objective function, introducing variables of the number of times of the standby insufficient scene and the total standby insufficient amount into the objective function, and obtaining a preset scheduling flexibility evaluation index value according to the variables;
the optimized scheduling model comprises: the aim is to minimize the cost of the thermal power generating unit and the cogeneration unit.
2. The method of claim 1, wherein the construction of the optimized scheduling model comprises:
determining a unit combination in front of the day according to the wind power, the electric load and the heat load prediction data in front of the day under the uncertain scene, and constructing an objective function by taking the minimum start-stop cost and the minimum operation cost of the thermal power unit and the minimum start-stop cost and the minimum operation cost of the cogeneration unit as targets;
the method is characterized in that the constraint conditions are CON unit power balance constraint, CON unit output upper and lower limit constraint, CON unit climbing constraint, CON unit transmission line capacity constraint, CHP unit power output constraint, CHP unit heat output constraint, thermodynamic system heating station constraint, thermodynamic system heat supply network constraint, thermodynamic system heat exchange station constraint and heat load constraint.
3. The method of claim 2, wherein the building of the security check model comprises:
setting a target function by taking the minimum wind curtailment and load shedding amount of the power system under an uncertain scene as a target;
meanwhile, the constraint conditions are the power balance constraint of the CON unit, the upper and lower limit constraint of the output of the CON unit, the climbing constraint of the CON unit, the capacity limit constraint of a power transmission line of the CON unit, the power output constraint of the CHP unit, the heat output constraint of the CHP unit, the heating station constraint of the thermodynamic system, the heat supply network constraint of the thermodynamic system, the heat exchange station constraint of the thermodynamic system and the heat load constraint;
introducing iteration variables and iteration times of the spare shortage scene times and the spare shortage total amount into the objective function;
based on the iteration variables and the iteration times of the spare shortage scene times and the spare shortage total amount, calculating an upper spare shortage total amount, an upper spare shortage scene times, a lower spare shortage total amount and a lower spare shortage scene occurrence time after iteration respectively when the upper spare shortage of the system and the lower spare shortage of the system exist;
and obtaining an index value corresponding to each scheduling flexibility evaluation index based on a preset scheduling flexibility evaluation index and the number of times of the insufficient up-regulation standby total amount, the insufficient up-regulation standby scene number, the insufficient down-regulation standby total amount, the insufficient down-regulation standby scene occurrence number and the iteration number after the iteration.
4. The method of claim 3, wherein the scheduling flexibility assessment indicator comprises: an up-regulation flexibility deficiency probability index, an up-regulation flexibility deficiency expectation index, a down-regulation flexibility deficiency probability index, and a down-regulation flexibility deficiency expectation index.
5. The method of claim 4, wherein the number of occurrences of the reduced reserve shortage scene and the total reduced reserve amount are calculated as follows:
Figure FDA0002501869720000021
in the formula, deltadownFor reducing the number of occurrences of a backup deficiency scenario, ηdownIn order to down-regulate the amount of insufficient reserve,
Figure FDA0002501869720000022
is the wind curtailment in the s-th iteration, T is the total time period, T is the time period, NwIs the total number of the wind power plant, and w is the wind power plant;
the number of times of the up standby shortage scene and the up standby shortage total amount are calculated as follows:
Figure FDA0002501869720000023
in the formula, deltaupFor upscaling with insufficient scene times, etaupIn order to adjust up the amount of the shortage,
Figure FDA0002501869720000024
is the load shedding at the s-th iteration, NdIs the total number of the load nodes, d is the load node;
the probability index with insufficient up-regulation flexibility, the expected index with insufficient up-regulation flexibility, the probability index with insufficient down-regulation flexibility and the expected index with insufficient down-regulation flexibility are respectively calculated according to the following formulas:
Figure FDA0002501869720000025
in the formula, PUFNS，tTo adjust up the probability index of insufficient flexibility, EUFNS，tTo adjust up the flexibility is not sufficiently desired, PDFNS，tTo adjust the probability of lack of flexibility down, EDFNS，tInsufficient flexibility is desired for turndown.
6. The method of claim 3, wherein determining an uncertain scene of a day-ahead wind, load based on historical wind data comprises:
the method comprises the following steps of (1) carrying out interval division on wind power output of an electric power system;
acquiring historical wind power data of each interval, and obtaining historical prediction error distribution of wind power output based on the historical wind power data of each interval;
determining a covariance matrix of a dynamic scene in the day ahead based on the historical prediction error distribution, and generating a plurality of uncertain scenes by using MATLAB based on the covariance matrix;
wherein the historical wind power data comprises: predicted and actual data of wind power and load.
7. The method of claim 6, wherein determining a covariance matrix for a dynamic scene in the future based on the historical prediction error distribution and generating a plurality of uncertain scenes using MATLAB based on the covariance matrix comprises:
calculating the covariance of any two multivariate normal random vectors in different time periods by adopting an exponential function method based on the historical prediction error distribution, and constructing a covariance matrix by the covariance to determine a day-ahead dynamic scene;
obtaining a sample of a multivariate normal random vector by adopting a mathematical algorithm based on a day-ahead dynamic scene determined by the covariance matrix;
based on the historical prediction error distribution, fitting by adopting an accumulative empirical probability distribution function based on a pre-obtained wind power prediction value to obtain a relative prediction error;
calculating to obtain an error scene based on the multivariate normal random vector sample and the relative prediction error;
and calculating to obtain an uncertain scene by adopting a cumulative probability distribution function of standard normal distribution based on the multivariate normal random vector sample and the error scene.
8. The method of claim 7, wherein the cumulative empirical probability distribution function is calculated as:
Figure FDA0002501869720000031
Figure FDA0002501869720000032
in the formula, FlTheta is a wind power random variable e and a sample delta for an accumulated empirical probability distribution function of a prediction errorkK is the number of historical wind power prediction data of each interval, deltakForecasting data for the interval historical wind power;
the uncertain scene is calculated as follows:
Φ(Zt)＝Fl(Δwt)
Δwt＝Fl -1(Φ(Zt))
in the formula, phi (-) is an uncertain scene obtained by cumulative calculation of a cumulative probability distribution function of a standard normal distribution, ZtFor multivariate normal random vector samples, Δ wtIs an error scenario.
9. A scheduling flexibility evaluation system of an electric heating combined system is characterized by comprising: the system comprises a scene module, a unit combination module and an evaluation module;
the scene module is used for determining uncertain scenes of day-ahead wind power and loads based on historical wind power data;
the unit combination module is used for substituting the day-ahead wind power, electric load and thermal load prediction data in the uncertain scene into a pre-constructed optimized scheduling model to determine a unit combination plan;
the evaluation module is used for evaluating the scheduling flexibility of the electric heating combined system by utilizing a pre-constructed safety check model based on the unit combination plan;
wherein the security check model comprises: taking the minimum of abandoned wind and cut load under an uncertain scene as an objective function, introducing variables of the number of times of the standby insufficient scene and the total standby insufficient amount into the objective function, and obtaining a preset scheduling flexibility evaluation index value according to the variables;
the optimized scheduling model comprises: the aim is to minimize the cost of the thermal power generating unit and the cogeneration unit.
10. The system of claim 9, wherein the crew assembly module comprises: an optimized scheduling model target submodule and an optimized scheduling model constraint submodule;
the optimized scheduling model target submodule is used for determining a unit combination in front of the day according to the wind power, the electric load and the heat load prediction data in the day under the uncertain scene, and constructing a target function by taking the minimum start-up and shut-down cost and the minimum operation cost of the thermal power unit and the minimum start-up and shut-down cost and the minimum operation cost of the cogeneration unit as targets;
and the optimized scheduling model constraint submodule is used for using the power balance constraint of the CON unit, the upper and lower limit constraint of the output of the CON unit, the climbing constraint of the CON unit, the capacity limit constraint of a power transmission line of the CON unit, the electrical output constraint of the CHP unit, the heat output constraint of the CHP unit, the heating station constraint of the thermodynamic system, the heat network constraint of the thermodynamic system, the heat exchange station constraint of the thermodynamic system and the heat load constraint as constraint conditions.
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