CN112700066A - Optimal time scale coordination method for scheduling of electric-thermal integrated energy system - Google Patents

Optimal time scale coordination method for scheduling of electric-thermal integrated energy system Download PDF

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CN112700066A
CN112700066A CN202110049118.3A CN202110049118A CN112700066A CN 112700066 A CN112700066 A CN 112700066A CN 202110049118 A CN202110049118 A CN 202110049118A CN 112700066 A CN112700066 A CN 112700066A
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scheduling
day
time
power
ahead
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CN112700066B (en
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董帅
林卓然
王杉
王守相
菅学辉
王绍敏
孙丰杰
李�昊
刘嘉超
夏天翔
孙宏宇
裴继坤
高连学
撖奥洋
钟世民
綦鲁波
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Tianjin Xianghe Electric Technology Co ltd
Qingdao Power Supply Co of State Grid Shandong Electric Power Co Ltd
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Tianjin Xianghe Electric Technology Co ltd
Qingdao Power Supply Co of State Grid Shandong Electric Power Co Ltd
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Abstract

The invention discloses an optimal scheduling time scale matching method for an electricity-heat comprehensive energy system, which comprises the following steps of: determining and acquiring basic operation parameters of the comprehensive energy system; acquiring day-ahead predicted values of wind power and load, and inputting the day-ahead predicted values into a pre-established day-ahead scheduling model to obtain a day-ahead scheduling result; acquiring the day short-term predicted values of wind power and load, inputting the day short-term predicted values into a day scheduling model, and selecting an optimal scheduling instruction period to obtain a day scheduling result; acquiring the ultra-short-term predicted values of wind power and load, and inputting the ultra-short-term predicted values into a real-time scheduling model to obtain a real-time scheduling result; the method can select the optimal scheduling time scale matching scheme of each device according to the control characteristics of various types of devices in different subsystems, and is favorable for the safety and the economy of the whole operation of the system.

Description

Optimal time scale coordination method for scheduling of electric-thermal integrated energy system
Technical Field
The invention relates to a multi-time scale scheduling scheme of an integrated energy system, in particular to an optimal time scale coordination method for scheduling of an electric-thermal integrated energy system.
Background
With the increasing social energy demand, the contradiction between energy supply and demand and the environmental pollution problem are increasingly revealed, and the construction of a comprehensive energy system with complementary multiple energies can realize the efficient clean utilization of multiple energies, and is a necessary choice for the development of the energy field. The control characteristics of the devices in each subsystem in the integrated energy system are obviously different, and the scheduling of the system is more difficult due to the inaccuracy of renewable energy and load prediction.
The prior art has the following defects and shortcomings: when multi-time scale scheduling is carried out, the scheduling periods of all subsystems in the same scheduling stage are always kept consistent, and the difference of the dynamic time scales of all subsystems is not taken into consideration. The response characteristics of the devices in each subsystem to the scheduling instruction and the role of the devices in the optimized scheduling are different, and the devices adopt a uniform scheduling instruction cycle, so that certain technical limitation exists in the actual execution, and the safety and the economy of the overall operation of the system are influenced.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides an optimal time scale matching method for scheduling of an electric-thermal integrated energy system, which considers the adjustment characteristics, variable working condition operation capacity and different functions in optimized scheduling of various types of equipment in each energy system and can reduce the influence on system scheduling caused by the deviation of the forecast data in the day ahead.
In order to solve the technical problem, the invention adopts the following technical scheme:
an optimal time scale coordination method for scheduling an electric-thermal integrated energy system comprises the following steps:
step 1: determining and acquiring basic operation parameters of the comprehensive energy system;
step 2: acquiring day-ahead predicted values of wind power and load, and inputting the day-ahead predicted values into a pre-established day-ahead scheduling model to obtain a day-ahead scheduling result;
and step 3: acquiring the day short-term predicted values of wind power and load, inputting the day short-term predicted values into a day scheduling model, and selecting an optimal scheduling instruction period to obtain a day scheduling result;
and 4, step 4: acquiring the ultra-short-term predicted values of wind power and load, and inputting the ultra-short-term predicted values into a real-time scheduling model to obtain a real-time scheduling result;
and 5: and (4) taking the output value of the real-time plan as an initial value predicted in a new round of days, and constraining the output deviation of the unit in the two dispatching plans.
Preferably, in step 2, the day-ahead scheduling model includes an objective function and a constraint condition of day-ahead scheduling, and the formulation thereof is performed by the following steps:
step 201: day-ahead scheduling objective function formulation
The day-ahead scheduling model takes the minimum total cost of the system as an objective function, namely:
Figure BDA0002898567730000021
wherein F is the total system cost; t is the scheduled cycle 24 hours before the day;
Figure BDA0002898567730000022
the power generation cost of the nth thermal power generating unit at the moment t is calculated;
Figure BDA0002898567730000023
the running cost of the nth CHP unit at the time t is obtained;
Figure BDA0002898567730000024
the power generation cost at time t for the nth gas turbine;
Figure BDA0002898567730000025
the heat production cost of the nth gas boiler at the time t;
Figure BDA0002898567730000026
the cost of purchasing and selling electricity at the time t;
further, the electricity purchasing and selling cost at the time t is as follows:
Figure BDA0002898567730000027
wherein the content of the first and second substances,
Figure BDA0002898567730000028
and
Figure BDA0002898567730000029
respectively the electricity purchasing power and the electricity selling power of the energy station and the superior power grid at the time t,
Figure BDA00028985677300000210
and
Figure BDA00028985677300000211
respectively obtaining the electricity purchasing price and the electricity selling price at the time t;
step 202: system energy balance constraint formulation
Power balance should be maintained in the system at all times, i.e.:
Figure BDA00028985677300000212
Figure BDA00028985677300000213
wherein the content of the first and second substances,
Figure BDA00028985677300000214
and
Figure BDA00028985677300000215
respectively the electric load and the heat load of the energy station at the moment t; p is a radical oft,jAnd ht,jThe subscript j ═ th, gt, wt, CHP, gb } corresponds to a thermal power generating unit, a gas turbine, a fan, a CHP unit, and a gas boiler, respectively;
Figure BDA00028985677300000216
and
Figure BDA00028985677300000217
the discharge power and the charge power of the electric energy storage equipment at the moment t are respectively;
Figure BDA00028985677300000218
and
Figure BDA00028985677300000219
respectively the heat release power and the heat charging power of the heat storage equipment at the moment t;
step 203: and (3) constraint making of the equipment model, namely:
Figure BDA00028985677300000220
wherein p ist,j,nThe output power of the nth device in the apparatus j;
Figure BDA00028985677300000221
and
Figure BDA00028985677300000222
respectively an upper limit and a lower limit of the output power of the nth device in the j; x is the number oft,j,nThe operation factor (a variable of 0-1) indicates whether the nth device in the equipment j is operated at the time t, and subscript j ═ { th, gt, CHP, gb } corresponds to a thermal power generating unit, a gas turbine, a CHP unit and a gas boiler respectively;
Figure BDA00028985677300000223
wherein p ist,j,n-pt-1,j,nThe difference value of the output power of the nth device in the device j at the moment t and the previous moment is obtained;
Figure BDA00028985677300000224
and
Figure BDA0002898567730000031
respectively, the landslide rate and the climbing rate of the nth device in the equipment j;
step 204: energy storage device constraint formulation
In order to facilitate scheduling consistency, the electric energy storage devices need to be ensured to have the same electric energy storage amount at the beginning and the end moments; ensuring that charging and discharging cannot be carried out simultaneously in each time period t, namely:
Figure BDA0002898567730000032
wherein p ist,essThe electric energy storage quantity of the electric energy storage equipment at the moment t;
Figure BDA0002898567730000033
an upper limit of the stored power of the electric energy storage equipment is set;
Figure BDA0002898567730000034
and
Figure BDA0002898567730000035
are respectively as
Figure BDA0002898567730000036
And
Figure BDA0002898567730000037
an upper power limit of (d);
Figure BDA0002898567730000038
and
Figure BDA0002898567730000039
charging factor and discharging factor (0-1 variable);
step 205: electricity purchase and sale restriction establishment
Ensuring that electricity purchasing and electricity selling can not be carried out simultaneously in each time period t, namely:
Figure BDA00028985677300000310
wherein the content of the first and second substances,
Figure BDA00028985677300000311
and
Figure BDA00028985677300000312
are respectively as
Figure BDA00028985677300000313
And
Figure BDA00028985677300000314
an upper power limit of (d);
Figure BDA00028985677300000315
and
Figure BDA00028985677300000316
the electricity purchasing factor and the electricity selling factor are respectively (0-1 variable).
Preferably, the intra-day scheduling model in step 3 includes an objective function and a constraint condition for intra-day scheduling, and the formulation thereof is performed by the following steps:
step 301: scheduling objective function formulation in day
The intra-day scheduling objective function is the same as the day-ahead scheduling objective function in step 201;
step 302: scheduling constraints formulation within a day
The intra-day scheduling constraints comprise system energy balance constraints, equipment model constraints, energy storage equipment constraints and electricity purchasing and selling constraints in the steps 202 to 205; on the basis, the intra-day scheduling increases the constraint of the output deviation of the intra-day scheduling and the pre-day scheduling unit, namely:
Figure BDA00028985677300000317
wherein the content of the first and second substances,
Figure BDA00028985677300000318
and
Figure BDA00028985677300000319
respectively are the output values of the nth device in the equipment j at the time t in day-ahead scheduling and rolling scheduling, and alpha is a constraint multiplier;
Figure BDA0002898567730000041
the upper limit of the output power of the nth device in the apparatus j.
Preferably, the optimal scheduling instruction cycle of each device in step 3 is selected in the following manner:
the power supply equipment is optimized by a three-stage scheduling system of day-ahead, day-inside and real-time on the premise of meeting self operation constraints;
combustion equipment (such as a gas boiler) bears a larger heat supply load proportion, has a slower adjusting rate and a larger adjustable capacity, and only participates in day-ahead and day-in two-stage scheduling, wherein the day-ahead scheduling period is kept unchanged; traversing all feasible scheduling instruction cycles in a rolling stage in the day, wherein the scheduling cycle obtained when the economy is optimal in the day is the optimal scheduling instruction cycle;
the regulation rate of the heat storage equipment is also at a slower level, the adjustable capacity is smaller, and the scheduling method is the same as that of combustion equipment;
the combined heat and power generation unit equipment has good quick adjustment capability and narrow adjustment capacity range, participates in three-stage scheduling of day-ahead, day-in-real time, keeps the scheduling period of rolling stages in day-ahead and day consistent with that of power supply equipment, participates in a real-time scheduling link, and keeps the power deviation to be minimum.
Preferably, the real-time scheduling model in step 4 includes an objective function and a constraint condition for real-time scheduling, and the formulation thereof is performed by the following steps:
step 401: real-time scheduling objective function formulation
The real-time scheduling model takes the minimum sum of the adjusting values of the output of each unit as a target function, wherein in order to improve the wind power consumption rate, a wind curtailment penalty term is considered in the target function, namely:
Figure BDA0002898567730000042
wherein P is the sum of output adjustment values of all devices at the moment t;
Figure BDA0002898567730000043
and
Figure BDA0002898567730000044
the output values of the nth device in the equipment j at the time t in real-time scheduling and rolling scheduling are respectively, and subscript j ═ th, gt, wt, CHP corresponds to a thermal power generating unit, a gas turbine, a fan and a CHP unit respectively; gamma is a wind abandon punishment coefficient; p is a radical oft,windGenerating power for the wind power plant at the time t; p is a radical oft,wtThe actual online power of the wind power at the moment t;
step 402: real-time scheduling constraint formulation
The real-time scheduling constraints comprise system energy balance constraints, equipment model constraints, energy storage equipment constraints and electricity purchasing and selling constraints in the steps 202 to 205; on the basis, real-time scheduling is formed by appointing on a basic operation point scheduled and made in the day, and the output deviation constraint of the unit is considered, namely:
Figure BDA0002898567730000045
wherein, beta is a constraint multiplier;
Figure BDA0002898567730000046
the upper limit of the output power of the nth device in the apparatus j.
Compared with the prior art, the scheme adopts the optimal time scale coordination method for the scheduling of the electric-thermal integrated energy system, and achieves the following beneficial effects:
(1) the regulation characteristics, variable working condition operation capability and the role of optimizing scheduling of various types of equipment in each energy system are taken into consideration, and the scheduling stage and the corresponding period of participation of each type of equipment in different subsystems are selected, so that the safety and the economy of the overall operation of the system are facilitated.
(2) The scheduling model comprises three time scales of 'day ahead-day in-real time', the load change is quickly tracked along with the reduction of the time scale, the prediction deviation in the day ahead can be well compensated, and the deviation between the optimization result of the scheduling model and a reference plan is small.
Drawings
Fig. 1 is a flow chart of a method for selecting an optimal time scale for scheduling an electric-thermal integrated energy system.
Fig. 2 is a cycle chart of a day-ahead scheduling plan according to an embodiment of the present invention.
Fig. 3 is a cycle chart of an intra-day dispatch plan in an embodiment of the present invention.
Fig. 4 is a cycle chart of a real-time dispatch plan according to an embodiment of the invention.
Fig. 5 is a diagram of a result of optimization of the power balance in the day-ahead scheduling according to the embodiment of the present invention.
Fig. 6 is a diagram of a result of a thermal balance optimization of a day-ahead scheduling according to an embodiment of the present invention.
FIG. 7 is a graph of deviation of total electrical power for various scheduling schemes in accordance with an embodiment of the present invention.
Fig. 8 is a graph of deviation of total thermal power for various scheduling schemes in accordance with an embodiment of the present invention.
Detailed Description
The invention provides an optimal time scale matching method for scheduling an electric-thermal integrated energy system, which considers the characteristics of different energy subsystems and equipment operation differences and comprises three time scale scheduling models of day-ahead-day-in-real-time. The electric system and the thermal system execute day-ahead scheduling plans of the same period; in the day scheduling, all feasible scheduling periods are traversed to select the time scale with the optimal economical efficiency.
The formula related by the scheme is as follows:
Figure BDA0002898567730000051
wherein F is the total system cost; t scheduled for day-aheadThe period is 24 hours;
Figure BDA0002898567730000052
the power generation cost of the nth thermal power generating unit at the moment t is calculated;
Figure BDA0002898567730000053
the running cost of the nth CHP unit at the time t is obtained;
Figure BDA0002898567730000054
the power generation cost at time t for the nth gas turbine;
Figure BDA0002898567730000055
the heat production cost of the nth gas boiler at the time t;
Figure BDA0002898567730000056
the cost of electricity purchase and sale at the time t.
Figure BDA0002898567730000057
Wherein the content of the first and second substances,
Figure BDA0002898567730000058
and
Figure BDA0002898567730000059
respectively the electricity purchasing power and the electricity selling power of the energy station and the superior power grid at the time t,
Figure BDA00028985677300000510
and
Figure BDA00028985677300000511
the electricity purchasing price and the electricity selling price at the moment t are respectively.
Figure BDA0002898567730000061
Figure BDA0002898567730000062
Wherein the content of the first and second substances,
Figure BDA0002898567730000063
and
Figure BDA0002898567730000064
respectively the electric load and the heat load of the energy station at the moment t; p is a radical oft,jAnd ht,jThe subscript j ═ th, gt, wt, CHP, gb } corresponds to a thermal power generating unit, a gas turbine, a fan, a CHP unit, and a gas boiler, respectively;
Figure BDA0002898567730000065
and
Figure BDA0002898567730000066
the discharge power and the charge power of the electric energy storage equipment at the moment t are respectively;
Figure BDA0002898567730000067
and
Figure BDA0002898567730000068
respectively the heat release power and the heat charging power of the heat storage equipment at the moment t.
Figure BDA0002898567730000069
Wherein p ist,j,nThe output power of the nth device in the apparatus j;
Figure BDA00028985677300000610
and
Figure BDA00028985677300000611
respectively an upper limit and a lower limit of the output power of the nth device in the j; x is the number oft,j,nFor the operating factor (variable 0-1), it means that the nth device in the apparatus j at time t isAnd if not, the subscript j is { th, gt, CHP, gb } and corresponds to a thermal power generating unit, a gas turbine, a CHP unit and a gas boiler respectively.
Figure BDA00028985677300000612
Wherein p ist,j,n-pt-1,j,nThe difference value of the output power of the nth device in the device j at the moment t and the previous moment is obtained;
Figure BDA00028985677300000613
and
Figure BDA00028985677300000614
respectively the landslide rate and the climbing rate of the nth device in plant j.
Figure BDA00028985677300000615
Wherein p ist,essThe electric energy storage quantity of the electric energy storage equipment at the moment t;
Figure BDA00028985677300000616
an upper limit of the stored power of the electric energy storage equipment is set;
Figure BDA00028985677300000617
and
Figure BDA00028985677300000618
are respectively as
Figure BDA00028985677300000619
And
Figure BDA00028985677300000620
an upper power limit of (d);
Figure BDA00028985677300000621
and
Figure BDA00028985677300000622
the charging factor and the discharging factor (variable 0-1) are respectively.
Figure BDA00028985677300000623
Wherein the content of the first and second substances,
Figure BDA00028985677300000624
and
Figure BDA00028985677300000625
are respectively as
Figure BDA00028985677300000626
And
Figure BDA00028985677300000627
an upper power limit of (d);
Figure BDA00028985677300000628
and
Figure BDA00028985677300000629
the electricity purchasing factor and the electricity selling factor are respectively (0-1 variable).
Figure BDA00028985677300000630
Wherein the content of the first and second substances,
Figure BDA0002898567730000071
and
Figure BDA0002898567730000072
respectively are the output values of the nth device in the equipment j at the time t in day-ahead scheduling and rolling scheduling, and alpha is a constraint multiplier;
Figure BDA0002898567730000073
the upper limit of the output power of the nth device in the apparatus j.
Figure BDA0002898567730000074
Wherein P is the sum of output adjustment values of all devices at the moment t;
Figure BDA0002898567730000075
and
Figure BDA0002898567730000076
the output values of the nth device in the equipment j at the time t in real-time scheduling and rolling scheduling are respectively, and subscript j ═ th, gt, wt, CHP corresponds to a thermal power generating unit, a gas turbine, a fan and a CHP unit respectively; gamma is a wind abandon punishment coefficient; p is a radical oft,windGenerating power for the wind power plant at the time t; p is a radical oft,wtThe actual online power of the wind power at the moment t.
Figure BDA0002898567730000077
Wherein, beta is a constraint multiplier;
Figure BDA0002898567730000078
the upper limit of the output power of the nth device in the apparatus j.
The scheme comprises the following contents:
(1) day-ahead scheduling model
The day-ahead scheduling is executed once every 24 hours, and the minimum total cost in the system scheduling period is taken as an objective function, as shown in formulas (1) to (2), and simultaneously, the constraint conditions of system operation and the constraint conditions of equipment operation need to be met, namely formulas (3) to (8). All devices participate in day-ahead scheduling. And (3) making a start-stop plan (namely start-stop state) of part of slow response equipment and basic operation points (namely equipment output) of all the equipment based on day-ahead prediction data of wind power and load. Because the thermal power generating unit usually has the minimum starting time and frequent adjustment is not beneficial to economy and safety, the starting and stopping states of the thermal power generating unit are not considered to be adjusted in a subsequent scheduling stage after the scheduling is finished day before.
(2) Scheduling model in day
The intra-day scheduling still aims at economy, the objective function is the same as the target function expressions (1) - (2) of the day-ahead scheduling, and besides the constraint condition expressions (3) - (8) of the day-ahead scheduling, the constraint condition expression (9) of the output deviation also needs to be met. And on the basis of the day-to-day short-term prediction data of wind power and load, the slow response equipment start-stop plan and the basic equipment operating point in the day-to-day plan, obtaining a day-to-day scheduling result according to a day-to-day scheduling model, rolling and updating the scheduling plan of the rest time period in one day, making the actual output of part of slow response equipment and changing the start-stop state of the quick start unit. The power supply equipment and the CHP unit take 1 hour as a cycle to participate in scheduling in the day; and traversing all feasible scheduling periods (namely 1 hour, 2 hours, 3 hours, 4 hours, 6 hours, 8 hours, 12 hours and 24 hours) by the combustion equipment and the heat storage equipment, obtaining a scheduling result of each period, and selecting a period with optimal economy as an optimal scheduling instruction period.
(3) Real-time scheduling model
And the real-time scheduling adjusts the modified day scheduling plan according to the real-time ultra-short term prediction data, and the target function of the real-time scheduling is the minimum sum of the adjustment values of the output of each unit. In order to encourage wind power to surf the internet, a wind abandon penalty term is added into the objective function, as shown in the formula (10). In addition to the constraint conditional expressions (3) to (8) of the day-ahead scheduling, the output deviation constraint expression (11) is also required to be satisfied. In real-time scheduling, the start and stop of each unit are unchanged, and the actual output of the quick response equipment is determined.
And (4) taking the output value of the real-time plan as an initial value of rolling prediction in a new round of days, and constraining the climbing of the unit in the two dispatching plans as the formula (6) to form closed-loop control. And scheduling the comprehensive energy system according to a scheduling result established by real-time scheduling.
The optimal time scale coordination method selection process of the electric-thermal integrated energy system scheduling is shown in fig. 1, and the principle and the steps are as follows:
1) initializing 101, namely initializing the allowable access position, the maximum installation number and the maximum installation power of the composite energy storage system;
2) acquiring day-ahead prediction data 102 (namely day-ahead prediction values) of wind power and load, inputting the day-ahead prediction values into a pre-established day-ahead scheduling model 103, and sending obtained day-ahead scheduling results into a day scheduling program, wherein the day-ahead scheduling results comprise start-stop states of partial slow response equipment;
3) acquiring the intra-day prediction data 104 (namely intra-day short-term prediction values) of wind power and load, inputting the intra-day short-term prediction values into the intra-day scheduling model, and traversing all selectable periods 105 (namely traversing all feasible scheduling instruction periods) in the intra-day scheduling model;
4) selecting a scheduling cycle obtained when the economy in the day is optimal as an optimal scheduling instruction cycle, namely determining an optimal scheduling time scale 106, inputting the scheduling cycle to a scheduling model 107 in the day to obtain a scheduling result in the day, wherein the scheduling result in the day comprises the actual output of part of slow response equipment and the start-stop state of a quick start unit, and sending the modified scheduling scheme to a real-time planning scheduling program;
5) acquiring real-time prediction data 108 (namely ultra-short-term prediction values) of wind power and load, inputting the ultra-short-term prediction values into a real-time scheduling model 109, acquiring a real-time scheduling result 110, wherein the real-time scheduling result comprises the actual output of all units, and scheduling the electricity-heat comprehensive energy system through the real-time scheduling result;
6) and (3) taking the output value of the real-time plan as an initial value predicted in a new round of days, and constraining the output deviation of the units and the like in the two dispatching plans to form a closed-loop control real-time dispatching result 110.
Example (b):
the optimal scheduling time scale cooperation method of the electricity-heat comprehensive energy system is based on a simplified energy station, and energy production equipment such as a thermal power unit (TH), a Gas Turbine (GT), a Gas Boiler (GB) and wind power (WT) are configured in the energy station; energy conversion devices, such as CHP; energy storage devices, such as Electrical Energy Storage (EES), Thermal Energy Storage (TES). The relevant parameters of each apparatus are shown in tables 1 and 2. The thermoelectric load, the wind power predicted value and the time-of-use electricity price are shown in table 3.
TABLE 1 thermal power generating unit operating parameters
Figure BDA0002898567730000081
Figure BDA0002898567730000091
TABLE 2 Equipment parameters
Figure BDA0002898567730000092
Figure BDA0002898567730000101
TABLE 3 thermoelectric load, wind power forecast, and electricity price parameters
Figure BDA0002898567730000102
Figure BDA0002898567730000111
The scheme is modeled by a YALMIP toolbox and solves the problem by Cplex12.8.0. Based on the optimal scheduling period selection method for the thermodynamic system, after traversing all the feasible scheduling instruction periods, the scheduling instruction periods of the devices in the system of the example are shown in table 4, where x in the table indicates that a certain device does not participate in the corresponding scheduling stage. And establishing a three-stage scheduling model of day before, day inside and real time as shown in figures 2-4, wherein the three-stage scheduling model comprises an objective function and constraint conditions for scheduling each stage. The scheduled scheduling period before the day is 24 hours, 1 hour is 1 scheduling period, and the scheduling period is updated 1 time every 24 hours. On the basis of a day-ahead plan, a rolling plan of the power system is updated for 1 time every 1 hour, and partial equipment of the thermal system is updated for 1 time every 4 hours and is responsible for rolling and updating a scheduling plan of the rest time period in 1 day; the real-time plan takes a rolling plan as a basic scheduling scheme, is updated for 1 time every 15 minutes, is responsible for scheduling the real-time scheduling plan of the next upcoming time period, and belongs to static optimization.
TABLE 4 scheduling instruction cycles for each device
Figure BDA0002898567730000112
Figure BDA0002898567730000121
Fig. 5 is an optimization result of each power subsystem in the day-ahead scheduling, and fig. 6 is an optimization result of each thermal subsystem in the day-ahead scheduling. As can be seen from fig. 5, during the electricity price valley period, the energy station meets the electricity load demand by purchasing electricity and charges the EES; at the moment, the output of the wind power plant is high, the output pressure of the gas turbine is the lowest level, and the wind power output is preferentially used to meet the electrical load requirement of the system. As can be seen from fig. 6, the heat load demand of the system is now mainly met by the gas boiler. Thereafter, as the electrical load level increases, the energy station meets the electrical balance requirement by increasing the GT output and releasing the ESS power, and surplus power is saved or sold to the grid by the ESS. Because the heat output and the electric output of the CHP satisfy a certain proportional relation, the heat output of the CHP is also obviously increased in the period, so that the heat output of the gas boiler is gradually reduced to become an auxiliary heat source. The heat load is supplied by GB and CHP, and TES supplements the balance heat load difference. The method is verified to effectively realize the multi-energy complementation.
Fig. 7 is a deviation curve of total electric power of each scheduling scheme, and fig. 8 is a deviation curve of total thermal power of each scheduling scheme. As can be seen from fig. 7 and 8, the deviation between the total electric power and the total thermal power of the unit in the conventional day-ahead plan and the actual reference plan is large; compared with a day-ahead plan, day-ahead-day scheduling is based on short-term rolling prediction information, and the deviation between a scheduling result and an actual load is smaller; the method provided by the invention can be used for quickly tracking the load change in a real-time scheduling stage by adjusting equipment with good performance on the basis of day-ahead-day scheduling, so that the actual load can be well matched, and the deviation between the optimization result of the multi-period scheduling model and a reference plan is small. In conclusion, the optimal time scale matching method for the scheduling of the electric-thermal comprehensive energy system provided by the invention has effectiveness and rationality.
It should be understood that the embodiments and examples discussed herein are for illustrative purposes only and that various modifications or changes in light thereof will be suggested to persons skilled in the art and are to be included within the purview of this application and scope of the appended claims.

Claims (5)

1. An optimal time scale matching method for scheduling of an electric-thermal integrated energy system is characterized by comprising the following steps:
step 1: determining and acquiring basic operation parameters of the comprehensive energy system;
step 2: acquiring day-ahead predicted values of wind power and load, and inputting the day-ahead predicted values into a pre-established day-ahead scheduling model to obtain a day-ahead scheduling result;
and step 3: acquiring the day short-term predicted values of wind power and load, inputting the day short-term predicted values into a day scheduling model, and selecting an optimal scheduling instruction period to obtain a day scheduling result;
and 4, step 4: acquiring the ultra-short-term predicted values of wind power and load, and inputting the ultra-short-term predicted values into a real-time scheduling model to obtain a real-time scheduling result;
and 5: and (4) taking the output value of the real-time plan as an initial value predicted in a new round of days, and constraining the output deviation of the unit in the two dispatching plans.
2. The method according to claim 1, wherein the 2 nd day scheduling model in the step comprises an objective function and a constraint condition of day-ahead scheduling, and the planning is performed by the following steps:
step 201: day-ahead scheduling objective function formulation
The day-ahead scheduling model takes the minimum total cost of the system as an objective function, namely:
Figure FDA0002898567720000011
wherein F is the total system cost; t is the scheduled cycle 24 hours before the day;
Figure FDA0002898567720000012
the power generation cost of the nth thermal power generating unit at the moment t is calculated;
Figure FDA0002898567720000013
the running cost of the nth CHP unit at the time t is obtained;
Figure FDA0002898567720000014
the power generation cost at time t for the nth gas turbine;
Figure FDA0002898567720000015
the heat production cost of the nth gas boiler at the time t;
Figure FDA0002898567720000016
the cost of purchasing and selling electricity at the time t;
further, the electricity purchasing and selling cost at the time t is as follows:
Figure FDA0002898567720000017
wherein the content of the first and second substances,
Figure FDA0002898567720000018
and
Figure FDA0002898567720000019
respectively the electricity purchasing power and the electricity selling power of the energy station and the superior power grid at the time t,
Figure FDA00028985677200000110
and
Figure FDA00028985677200000111
are respectively asthe electricity purchasing price and the electricity selling price at the time t;
step 202: system energy balance constraint formulation
Power balance should be maintained in the system at all times, i.e.:
Figure FDA00028985677200000112
Figure FDA00028985677200000113
wherein the content of the first and second substances,
Figure FDA0002898567720000021
and
Figure FDA0002898567720000022
respectively the electric load and the heat load of the energy station at the moment t; p is a radical oft,jAnd ht,jThe subscript j ═ th, gt, wt, CHP, gb } corresponds to a thermal power generating unit, a gas turbine, a fan, a CHP unit, and a gas boiler, respectively;
Figure FDA0002898567720000023
and
Figure FDA0002898567720000024
the discharge power and the charge power of the electric energy storage equipment at the moment t are respectively;
Figure FDA0002898567720000025
and
Figure FDA0002898567720000026
respectively the heat release power and the heat charging power of the heat storage equipment at the moment t;
step 203: and (3) constraint making of the equipment model, namely:
Figure FDA0002898567720000027
wherein p ist,j,nThe output power of the nth device in the apparatus j;
Figure FDA0002898567720000028
and
Figure FDA0002898567720000029
respectively an upper limit and a lower limit of the output power of the nth device in the j; x is the number oft,j,nThe index j ═ th, gt, CHP, gb } corresponds to a thermal power generating unit, a gas turbine, a CHP unit, and a gas boiler, respectively;
Figure FDA00028985677200000210
wherein p ist,j,n-pt-1,j,nThe difference value of the output power of the nth device in the device j at the moment t and the previous moment is obtained;
Figure FDA00028985677200000211
and
Figure FDA00028985677200000212
respectively, the landslide rate and the climbing rate of the nth device in the equipment j;
step 204: energy storage device constraint formulation
In order to facilitate scheduling consistency, the electric energy storage devices need to be ensured to have the same electric energy storage amount at the beginning and the end moments; ensuring that charging and discharging cannot be carried out simultaneously in each time period t, namely:
Figure FDA00028985677200000213
wherein p ist,essThe electric energy storage quantity of the electric energy storage equipment at the moment t;
Figure FDA00028985677200000214
an upper limit of the stored power of the electric energy storage equipment is set;
Figure FDA00028985677200000215
and
Figure FDA00028985677200000216
are respectively as
Figure FDA00028985677200000217
And
Figure FDA00028985677200000218
an upper power limit of (d);
Figure FDA00028985677200000219
and
Figure FDA00028985677200000220
charging factors and discharging factors are respectively;
step 205: electricity purchase and sale restriction establishment
Ensuring that electricity purchasing and electricity selling can not be carried out simultaneously in each time period t, namely:
Figure FDA00028985677200000221
wherein the content of the first and second substances,
Figure FDA0002898567720000031
and
Figure FDA0002898567720000032
are respectively as
Figure FDA0002898567720000033
And
Figure FDA0002898567720000034
an upper power limit of (d);
Figure FDA0002898567720000035
and
Figure FDA0002898567720000036
the electricity purchasing factor and the electricity selling factor are respectively.
3. The method for matching the optimal time scale for scheduling the electric-thermal integrated energy system according to claim 2, wherein the intra-day scheduling model in the step 3 comprises an objective function and constraint conditions for intra-day scheduling, and the method comprises the following steps:
step 301: scheduling objective function formulation in day
The intra-day scheduling objective function is the same as the day-ahead scheduling objective function in step 201;
step 302: scheduling constraints formulation within a day
The intra-day scheduling constraints comprise system energy balance constraints, equipment model constraints, energy storage equipment constraints and electricity purchasing and selling constraints in the steps 202 to 205; on the basis, the intra-day scheduling increases the constraint of the output deviation of the intra-day scheduling and the pre-day scheduling unit, namely:
Figure FDA0002898567720000037
wherein the content of the first and second substances,
Figure FDA0002898567720000038
and
Figure FDA0002898567720000039
respectively are the output values of the nth device in the equipment j at the time t in day-ahead scheduling and rolling scheduling, and alpha is a constraint multiplier;
Figure FDA00028985677200000310
the upper limit of the output power of the nth device in the apparatus j.
4. The method for matching the optimal scheduling time scale of the electric-thermal integrated energy system according to claim 1, wherein the optimal scheduling command period of each device in the step 3 is selected as follows:
the power supply equipment is optimized by a three-stage scheduling system of day-ahead, day-inside and real-time on the premise of meeting self operation constraints;
the combustion equipment bears a larger heat supply load proportion, has a slower adjusting rate and a larger adjustable capacity, and only participates in the day-ahead and day-in two-stage scheduling, wherein the day-ahead scheduling period is kept unchanged; traversing all feasible scheduling instruction cycles in a rolling stage in the day, wherein the scheduling cycle obtained when the economy is optimal in the day is the optimal scheduling instruction cycle;
the regulation rate of the heat storage equipment is also at a slower level, the adjustable capacity is smaller, and the scheduling method is the same as that of combustion equipment;
the combined heat and power generation unit equipment has good quick adjustment capability and narrow adjustment capacity range, participates in three-stage scheduling of day-ahead, day-in-real time, keeps the scheduling period of rolling stages in day-ahead and day consistent with that of power supply equipment, participates in a real-time scheduling link, and keeps the power deviation to be minimum.
5. The method for matching the optimal time scale for scheduling the electric-thermal integrated energy system according to claim 2, wherein the real-time scheduling model in the step 4 comprises an objective function and a constraint condition for real-time scheduling, and the method comprises the following steps:
step 401: real-time scheduling objective function formulation
The real-time scheduling model takes the minimum sum of the adjusting values of the output of each unit as a target function, wherein in order to improve the wind power consumption rate, a wind curtailment penalty term is considered in the target function, namely:
Figure FDA0002898567720000041
wherein P is the sum of output adjustment values of all devices at the moment t;
Figure FDA0002898567720000042
and
Figure FDA0002898567720000043
the output values of the nth device in the equipment j at the time t in real-time scheduling and rolling scheduling are respectively, and subscript j ═ th, gt, wt, CHP corresponds to a thermal power generating unit, a gas turbine, a fan and a CHP unit respectively; gamma is a wind abandon punishment coefficient; p is a radical oft,windGenerating power for the wind power plant at the time t; p is a radical oft,wtThe actual online power of the wind power at the moment t;
step 402: real-time scheduling constraint formulation
The real-time scheduling constraints comprise system energy balance constraints, equipment model constraints, energy storage equipment constraints and electricity purchasing and selling constraints in the steps 202 to 205; on the basis, real-time scheduling is formed by appointing on a basic operation point scheduled and made in the day, and the output deviation constraint of the unit is considered, namely:
Figure FDA0002898567720000044
wherein, beta is a constraint multiplier;
Figure FDA0002898567720000045
the upper limit of the output power of the nth device in the apparatus j.
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