CN113902339A - Optimized scheduling method and device for comprehensive energy system - Google Patents

Optimized scheduling method and device for comprehensive energy system Download PDF

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CN113902339A
CN113902339A CN202111329448.4A CN202111329448A CN113902339A CN 113902339 A CN113902339 A CN 113902339A CN 202111329448 A CN202111329448 A CN 202111329448A CN 113902339 A CN113902339 A CN 113902339A
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period
heat pump
formula
time
storage device
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龙浩
张文栋
刘子琨
刘晨
梁涛
葛群
刘亚祥
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State Power Investment Group Hunan Comprehensive Smart Energy Co ltd
Wuling Power Corp Ltd
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State Power Investment Group Hunan Comprehensive Smart Energy Co ltd
Wuling Power Corp Ltd
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    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/06—Power analysis or power optimisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/08—Thermal analysis or thermal optimisation

Abstract

The invention discloses an optimized scheduling method and device of a comprehensive energy system, wherein the method comprises the steps of obtaining performance parameters, time-by-time operation parameters and time-by-time required values of energy utilization equipment of the comprehensive energy system in a period to be scheduled; constructing a dynamic programming model, wherein the decision optimization objective of the dynamic programming model is that the total operation cost of each time interval in a scheduling cycle is minimum, and a state transition equation of the dynamic programming model is determined according to a performance parameter, a time-by-time operation parameter and a time-by-time demand value; optimizing the dynamic planning model and determining a scheduling scheme of the comprehensive energy system. The invention considers the change of the heat pump performance parameters along with the operation condition and the coupling utilization of the heat pump, the stored energy and the like, and solves the problem of underground soil heat unbalance caused by a ground source heat pump in a coupling energy system. The invention solves the energy system scheduling problem by using a genetic algorithm based on dynamic programming, gives consideration to the global optimization and rapidity of the scheduling problem solution, and can ensure that the system can operate efficiently, save energy and stably for a long time.

Description

Optimized scheduling method and device for comprehensive energy system
Technical Field
The invention belongs to the technical field of operation optimization of an integrated energy system, and particularly relates to an optimized scheduling method and device of the integrated energy system.
Background
At present, the method realizes energy diversification, relieves the dependence and constraint on limited mineral energy, and is one of the important measures of energy development strategy and energy structure adjustment in China. The heat pump is a device capable of converting low-level heat energy which cannot be directly utilized into high-level heat energy which can be utilized, is one of main devices for providing heat, and is widely applied to many fields due to the characteristics of environmental friendliness, energy conservation and the like. Different types of heat pumps have respective advantages and disadvantages when applied due to different energy forms. If the heat of the air source heat pump is easy to obtain, the system installation is less influenced by the site, the installation is simple and convenient, the system structure is simple, but the unit efficiency is lower, the efficiency is greatly influenced by the outdoor temperature, the operation efficiency is low in a cold day, and even the air source heat pump cannot be normally used; the heat required by the ground source heat pump system is provided by the underground rock-soil layer, and the system is not easily influenced by the environmental temperature like an air source heat pump, but the problem of underground soil heat unbalance is easily caused; the water source heat pump has the advantages of stable and reliable operation, good heat exchange effect, high heat exchange efficiency, small seasonal fluctuation, easy pollution and waste of water resources, high requirement on water quality and large initial investment.
In consideration of complementarity of various heat pumps, a great deal of attempts are made in academic circles and engineering circles on combined application of various heat pumps, and a multi-heat-pump coupled comprehensive energy system is widely applied to scenes such as parks and buildings at present, but due to the lack of a complete optimization scheduling strategy, the operation of the system is often based on manual experience, the optimal operation state is difficult to maintain, and the efficient energy conservation and long-term stable operation of the whole system cannot be guaranteed, so that the operation cost is increased.
Disclosure of Invention
The invention provides an optimal scheduling method and device for an integrated energy system, and solves the technical problem that the existing integrated energy system cannot operate efficiently, energy-saving and stably for a long time.
The first aspect of the invention discloses an optimized scheduling method of an integrated energy system, which comprises the following steps:
acquiring performance parameters, time-by-time operation parameters and time-by-time required values of energy utilization equipment in the comprehensive energy system in a period to be scheduled;
constructing a dynamic programming model, wherein the decision optimization objective of the dynamic programming model is that the total operation cost of each time interval in a scheduling cycle is minimum, and a state transition equation of the dynamic programming model is determined according to the performance parameters, the time-by-time operation parameters and the time-by-time demand values;
optimizing the dynamic planning model and determining a scheduling scheme of the comprehensive energy system.
Preferably, the energy supply equipment comprises a heat storage device, a cold storage device and a heat pump unit;
the heat storage device and the cold storage device are both connected with a heat pump unit;
the heat pump unit comprises a plurality of heat pumps, and the heat pump is at least one of an air source heat pump, a water source heat pump and a ground source heat pump.
Preferably, the state transition equations of the dynamic programming model include a state transition equation corresponding to the heat storage device and a state transition equation corresponding to the cold storage device;
the state transition equation corresponding to the heat storage device is as a first formula, and the first formula is as follows:
Figure BDA0003348206450000021
in the formula (I), the compound is shown in the specification,
Figure BDA0003348206450000022
the heat storage capacity of the heat storage device at the end of the t period in the cycle to be scheduled,
Figure BDA0003348206450000023
the heat storage capacity delta of the heat storage device at the end of the t-1 time period in the cycle to be scheduledhAs the dissipation factor of the thermal storage device,
Figure BDA0003348206450000024
in order to achieve the heat storage efficiency of the heat storage device,
Figure BDA0003348206450000025
for the heat release efficiency of the heat storage device, delta t is the duration of t time period in the period to be scheduled, Vt hDetermining according to a second formula, the second formula being:
Figure BDA0003348206450000026
in the formula (I), the compound is shown in the specification,
Figure BDA0003348206450000027
is the output value M of the heating capacity of the ith heat pump in the heat pump unit in the t periodHPThe total number of heat pumps in the heat pump unit,
Figure BDA0003348206450000028
a heat load demand value for the energy-consuming equipment at the time period t;
the state transition equation corresponding to the cold accumulation device is as a third formula, and the third formula is as follows:
Figure BDA0003348206450000029
in the formula (I), the compound is shown in the specification,
Figure BDA00033482064500000210
the cold storage capacity of the cold storage device at the end of the t period in the period to be scheduled,
Figure BDA00033482064500000211
is the cold accumulation capacity delta of the cold accumulation device at the end of the t-1 time period in the period to be scheduledcIn order to obtain the dissipation coefficient of the cold accumulation device,
Figure BDA00033482064500000212
in order to achieve the cold storage efficiency of the cold storage device,
Figure BDA00033482064500000213
for the cold release efficiency of the cold storage device, delta t is the duration of t time period in the period to be scheduled, Vt cDetermined according to a fourth formula, which is:
Figure BDA00033482064500000214
in the formula (I), the compound is shown in the specification,
Figure BDA00033482064500000215
is the output value M of the ith heat pump refrigerating capacity in the heat pump unit in the t periodHPThe total number of heat pumps in the heat pump unit,
Figure BDA00033482064500000216
the cold load demand value of the energy consuming device for the period t.
Preferably, the
Figure BDA00033482064500000217
Is determined according to a fifth formula, which is:
Figure BDA00033482064500000218
in the formula (I), the compound is shown in the specification,
Figure BDA00033482064500000219
the input power of the heating of the ith heat pump in the heat pump unit in the t period,
Figure BDA00033482064500000220
the method comprises the steps that the heating coefficient of the ith heat pump in a heat pump unit in a t-period is determined according to working condition parameters of the ith heat pump;
the above-mentioned
Figure BDA0003348206450000031
Is determined according to a sixth formula, said sixth formula being:
Figure BDA0003348206450000032
in the formula (I), the compound is shown in the specification,
Figure BDA0003348206450000033
is the refrigeration input power of the ith heat pump in the heat pump unit in the t period,
Figure BDA0003348206450000034
the method is characterized in that the refrigeration coefficient of the ith heat pump in the heat pump unit in the t period is determined according to the working condition parameter of the ith heat pump.
Preferably, when a ground source heat pump is included in the heat pumps, the state transition equation of the dynamic programming model further includes a state transition equation corresponding to the ground source heat pump;
the state transition equation corresponding to the ground source heat pump is determined according to a seventh formula, wherein the seventh formula is as follows:
Figure BDA0003348206450000035
in the formula (I), the compound is shown in the specification,
Figure BDA0003348206450000036
the total amount of heat extracted from the soil or released to the soil by the ground source heat pump at the end of the t period in the period to be scheduled,
Figure BDA0003348206450000037
the total amount of heat extracted from the soil or released to the soil by the ground source heat pump at the end of the t-1 time period in the period to be scheduled,
Figure BDA0003348206450000038
for the output value of the heating capacity of the ground source heat pump in the period t,
Figure BDA0003348206450000039
is the output value, COP, of the refrigerating capacity of the ground source heat pump in the t periodt GHP,hThe heating coefficient, COP, of the ground source heat pump in the t periodt GHP,cThe refrigeration coefficient of the ground source heat pump in the t period is shown.
Preferably, the
Figure BDA00033482064500000310
The constraint condition to be satisfied is an eighth formula, where the eighth formula is:
Figure BDA00033482064500000311
Figure BDA00033482064500000312
in the formula (I), the compound is shown in the specification,
Figure BDA00033482064500000313
and
Figure BDA00033482064500000314
respectively is the optimal output reference value of the ground source heat pump on the dispatching day d,
Figure BDA00033482064500000315
and
Figure BDA00033482064500000316
the output lower limit adjustment coefficient and the output upper limit adjustment coefficient on the dispatching day d when the ground source heat pump heats,
Figure BDA00033482064500000317
and
Figure BDA00033482064500000318
respectively is the output lower limit adjusting coefficient and the upper limit adjusting coefficient of the ground source heat pump on the dispatching day d during refrigeration,
Figure BDA00033482064500000319
and
Figure BDA00033482064500000320
respectively taking the total amount of heat extraction (heating) from the soil or heat release (refrigeration) to the soil for the scheduling day d of the ground source heat pump, and taking the T time period as the last time period T in the heating or refrigeration mode for the scheduling day d
Figure BDA00033482064500000321
Corresponding form.
Preferably, the energy supply equipment further comprises an electrical storage device, and correspondingly, the state transition equation of the dynamic programming model further comprises a state transition equation corresponding to the electrical storage device;
the state transition equation corresponding to the power storage device is a ninth formula, and the ninth formula is as follows:
Figure BDA0003348206450000041
in the formula, SOCtState of charge, SOC, of the electrical storage device at the end of time period t in the cycle to be scheduledt-1The state of charge of the electrical storage device at the end of the t-1 time period in the scheduling cycle,
Figure BDA0003348206450000042
in order to achieve the charging efficiency of the electrical storage device,
Figure BDA0003348206450000043
for the discharge efficiency of the accumulator device, ErRated capacity of the accumulator device, Vt eDetermined according to a tenth formula, which is:
Figure BDA0003348206450000044
in the formula (I), the compound is shown in the specification,
Figure BDA0003348206450000045
and
Figure BDA0003348206450000046
the interactive power, the photovoltaic power generation power and the fan power generation power of an external power grid connected with the power storage device in the time period t are respectively,
Figure BDA0003348206450000047
for the electrical load demand value of the energy consuming device for the period t,
Figure BDA0003348206450000048
is the input power of the ith heat pump in the heat pump unit in the t period, MHPThe total number of heat pumps in the heat pump unit.
Preferably, the decision optimization objective of the dynamic programming model is as an eleventh formula, where the eleventh formula is:
Figure BDA0003348206450000049
in the formula (I), the compound is shown in the specification,
Figure BDA00033482064500000410
the maintenance cost of the power output of the ith power supply equipment unit,
Figure BDA00033482064500000411
for the power supply output, Ep, of the ith power supply device during the period ttIs the electricity rate for the period of time t,
Figure BDA00033482064500000412
is the electricity purchasing quantity in the period of t, XtIs a decision variable and
Figure BDA00033482064500000413
Figure BDA00033482064500000414
for the ground source heat pump to extract the total heat from the soil or release the total heat to the soil in the period to be scheduled,
Figure BDA00033482064500000415
the heat storage capacity of the heat storage device at the end of the t period in the cycle to be scheduled,
Figure BDA00033482064500000416
for the cold storage capacity, SOC, of the cold storage device at the end of the t period in the period to be scheduledtAnd the charge state of the power storage device at the end of the t period in the period to be scheduled.
Preferably, optimizing the dynamic programming model specifically includes:
and optimizing the dynamic programming model by utilizing a genetic algorithm.
The second aspect of the present invention discloses an optimized scheduling device for an integrated energy system, comprising:
the information acquisition module is used for acquiring performance parameters, time-by-time operation parameters and time-by-time required values of energy utilization equipment in the comprehensive energy system in a period to be scheduled;
the model building module is used for building a dynamic planning model, the decision optimization goal of the dynamic planning model is that the total operation cost of each time interval in a scheduling cycle is minimum, and a state transition equation of the dynamic planning model is determined according to the performance parameters, the time-by-time operation parameters and the time-by-time demand values;
and the scheduling scheme determining module is used for optimizing the dynamic planning model and determining the scheduling scheme of the comprehensive energy system.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides an optimal scheduling method and device for an integrated energy system, which are particularly suitable for a multi-heat-pump coupled integrated energy system.
The method of the invention fully considers the change of the heat pump performance parameters along with the operation working condition and the coupling utilization of photovoltaic, heat pump, energy storage and the like, and is closer to the actual situation. When the ground source heat pump exists, cold-heat balance operation constraint of the ground source heat pump is fully considered, the best output plan is solved through the ground source heat pump daily output optimization model, the best output plan constraint of the ground source heat pump is established, and the problem of underground soil heat imbalance is avoided. Furthermore, the invention provides a genetic algorithm based on dynamic programming to solve the comprehensive energy scheduling problem of multi-heat-pump coupling, gives consideration to the global optimization and rapidity of the scheduling problem solution, can reduce the system operation cost, and better guides the economic, efficient and environment-friendly operation of the multi-heat-pump coupling comprehensive energy system.
Drawings
FIG. 1 is a flow chart of the optimal scheduling method of the integrated energy system of the present invention;
fig. 2 is a schematic structural diagram of the comprehensive energy system optimization scheduling device of the invention.
In the figure, 101 is an information acquisition module, 102 is a model construction module, and 103 is a scheduling scheme determination module.
Detailed Description
The technical solution of the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments. It is to be understood that the following examples are only illustrative and explanatory of the present invention and should not be construed as limiting the scope of the present invention. All the technologies realized based on the above-mentioned contents of the present invention are covered in the protection scope of the present invention.
The first aspect of the disclosure discloses an optimal scheduling method for an integrated energy system, as shown in fig. 1, including:
step 1, acquiring performance parameters, time-by-time operation parameters and time-by-time required values of energy utilization equipment in the comprehensive energy system in a period to be scheduled.
The energy supply equipment in the comprehensive energy system can only comprise a heat storage device and a cold storage device which are connected with the heat pump unit; the heat pump unit comprises a plurality of heat pumps, and the heat pump is at least one of an air source heat pump, a water source heat pump and a ground source heat pump.
The time-by-time output model of the heat pump in the heat pump unit is as follows:
heating mode:
Figure BDA0003348206450000051
a refrigeration mode:
Figure BDA0003348206450000052
in the formula (I), the compound is shown in the specification,
Figure BDA0003348206450000053
the unit is kW which is the input power of heating and refrigerating of the heat pump unit in the period t;
Figure BDA0003348206450000054
outputting the heating capacity and the refrigerating capacity of the heat pump unit in kW for the time period t;
Figure BDA0003348206450000061
the heating and refrigerating performance coefficients of the heat pump unit in the t period are shown. Wherein the COP of the heat pump unit is influenced by various operating parameters, e.g. the load factor ptWater supply temperature Tt 0Ambient temperature Tt eAnd so on, and, therefore,
COPt＝f(pt,Tt 0,Tt e) (3)
as an example of an implementation form, the coefficient of performance of the air source heat pump can be fitted to a second order polynomial through the data:
Figure BDA0003348206450000062
for a ground source heat pump, the coefficient of performance of the ground source heat pump is less influenced by the ambient temperature and can be simplified to be
Figure BDA0003348206450000063
The heat storage device and the cold storage device have the time-by-time output model as follows:
a heat storage device:
Figure BDA0003348206450000064
cold storage device:
Figure BDA0003348206450000065
in the formula (I), the compound is shown in the specification,
Figure BDA0003348206450000066
the unit of the heat storage capacity of the heat storage device and the unit of the cold storage capacity of the cold storage device are kWh respectively at the end of the t period;
Figure BDA0003348206450000067
and
Figure BDA0003348206450000068
the unit of the heat storage power and the unit of the heat release power of the heat storage device in the t time period are kW;
Figure BDA0003348206450000069
and
Figure BDA00033482064500000610
the cold accumulation power and the cold discharge power of the cold accumulation device in the t time period are respectively, and the unit is kW; deltahAnd deltacThe dissipation coefficients of the heat storage device and the cold storage device are respectively;
Figure BDA00033482064500000611
and
Figure BDA00033482064500000612
respectively the cold accumulation efficiency and the cold discharge efficiency of the cold accumulation device,
Figure BDA00033482064500000613
and
Figure BDA00033482064500000614
the thermal storage efficiency and the heat release efficiency of the thermal storage device are respectively.
The energy supply equipment in the comprehensive energy system can also comprise a heat storage device, a cold storage device and an electric storage device. The power storage device is connected with an external power grid, a photovoltaic power generation device, a wind power generation device and the like. The heat storage device and the cold storage device are both connected with a heat pump unit, the heat pump unit comprises a plurality of heat pumps, and the heat pumps are at least one of air source heat pumps, water source heat pumps and ground source heat pumps.
When the storage device is included, the time-by-time output model of the storage battery is expressed as:
Figure BDA00033482064500000615
in the formula, SOCtA state of charge of the electrical storage device at the end of the t period;
Figure BDA00033482064500000616
the charging power and the discharging power of the power storage device in the t time period are respectively in kW;
Figure BDA00033482064500000617
the electrical storage device charging efficiency and discharging efficiency, respectively; erIs the rated capacity of the electrical storage device.
The comprehensive energy system operates on the basis of meeting the constraint of the equipment output model, and also needs to meet the constraints of the balance of cooling, heating and power, the constraint of the equipment output, the constraint of the storage and discharge of an energy storage device, the constraint of the transmission capacity of an external power grid and the like, and the constraint expression is as follows:
Figure BDA0003348206450000071
Figure BDA0003348206450000072
Figure BDA0003348206450000073
Figure BDA0003348206450000074
Figure BDA0003348206450000075
Figure BDA0003348206450000076
Figure BDA0003348206450000077
Figure BDA0003348206450000078
Figure BDA0003348206450000079
Figure BDA00033482064500000710
Figure BDA00033482064500000711
Figure BDA00033482064500000712
SOCmin≤SOCt≤SOCmax (21)
Figure BDA00033482064500000713
in the formula (I), the compound is shown in the specification,
Figure BDA00033482064500000714
respectively setting the electricity, heat and cold load demand values in the period t;
Figure BDA00033482064500000715
the comprehensive energy system and the external power grid interaction power, the photovoltaic power generation power and the fan power generation power are respectively in the t period;
Figure BDA00033482064500000716
respectively representing the upper limit and the lower limit of the output of the heat pump i;
Figure BDA00033482064500000717
the upper limit and the lower limit of the heat storage power, the upper limit and the lower limit of the heat release power and the upper limit and the lower limit of the heat storage quantity of the heat storage device are respectively set;
Figure BDA00033482064500000718
the cold accumulation device comprises an upper limit and a lower limit of cold accumulation power, an upper limit and a lower limit of cold release power and an upper limit and a lower limit of cold accumulation amount;
Figure BDA00033482064500000719
SOCmax，SOCminrespectively setting the upper and lower limits of charging power, the upper and lower limits of discharging power and the upper and lower limits of state of charge of the storage battery;
Figure BDA00033482064500000720
Figure BDA00033482064500000721
the maximum selling capacity upper limit and the maximum buying capacity upper limit are interacted between the comprehensive energy system and an external power grid.
In order to ensure that the scheduling result of the scheduling cycle does not affect the next scheduling cycle, the energy storage state of the energy storage equipment at the end of the scheduling cycle is required to be restored to be close to the initial value of the scheduling cycle, namely:
Figure BDA00033482064500000722
Figure BDA00033482064500000723
-ΔSOC≤SOCT-SOC0≤ΔSOC (25)
in the formula,. DELTA.Sh，ΔScThe delta SOC is the absolute value of the deviation allowed by the heat storage device, the cold storage device and the power storage device at the beginning and the end of the dispatching period respectively; and T is the last period in the cycle to be scheduled.
The hourly demand value of the energy utilization equipment in the comprehensive energy system is determined according to factors such as weather, day type and the like in a period to be scheduled.
The period to be scheduled in the present invention may be several hours, one day, several days, several months, etc.
And 2, constructing a dynamic programming model, wherein the decision optimization objective of the dynamic programming model is that the total operation cost of each time interval in a scheduling cycle is minimum, and a state transition equation of the dynamic programming model is determined according to the performance parameters, the time-by-time operation parameters and the time-by-time required value.
The invention adopts a forward dynamic planning method to convert the scheduling problem of the comprehensive energy system into a multi-stage decision problem and establish a dynamic planning model of the comprehensive energy system.
Aiming at the problem of optimizing and scheduling the comprehensive energy system, decision-making stage division is carried out on the scheduling problem according to a time domain.
In the method for optimizing and scheduling the comprehensive energy system, the scheduling objective is that the system operation cost is minimum, and a specific function is expressed as follows:
Figure BDA0003348206450000081
and F represents the total running cost of the system, including the equipment operation and maintenance cost, the outsourcing electricity cost and the like. T is the last period of the cycle to be scheduled, N is the total number of devices in the comprehensive energy system,
Figure BDA0003348206450000082
the corresponding operation cost is output for the unit energy supply of the equipment i,
Figure BDA0003348206450000083
for the energy supply output of the device i during the period t, Ept，
Figure BDA0003348206450000084
Respectively the electricity price and the purchased electricity quantity in the t period.
On the basis of the formula (26), the decision optimization objective of the dynamic programming model is that the total operating cost of each time interval in the scheduling cycle is minimum, specifically:
Figure BDA0003348206450000085
in the formula (I), the compound is shown in the specification,
Figure BDA0003348206450000086
the maintenance cost of the power output of the ith power supply equipment unit,
Figure BDA0003348206450000087
is as followsEnergy supply output, Ep, of i energy supply devices in a time period ttIs the electricity rate for the period of time t,
Figure BDA0003348206450000088
is the electricity purchasing quantity in the period of t, XtIs a decision variable and
Figure BDA0003348206450000089
wherein the content of the first and second substances,
Figure BDA00033482064500000810
for the ground source heat pump to extract the total heat from the soil or release the total heat to the soil in the period T to be scheduled,
Figure BDA00033482064500000811
the heat storage capacity of the heat storage device at the end of the t period in the cycle to be scheduled,
Figure BDA00033482064500000812
for the cold storage capacity, SOC, of the cold storage device at the end of the t period in the period to be scheduledtAnd the charge state of the power storage device at the end of the t period in the period to be scheduled.
When the energy supply equipment only comprises a heat storage device and a cold storage device which are connected with the heat pump unit, the state transfer equation of the dynamic programming model comprises a state transfer equation corresponding to the heat storage device and a state transfer equation corresponding to the cold storage device;
the state transition equation corresponding to the thermal storage device is as in equation (28):
Figure BDA0003348206450000091
in the formula (I), the compound is shown in the specification,
Figure BDA0003348206450000092
the heat storage capacity of the heat storage device at the end of the t period in the cycle to be scheduled,
Figure BDA0003348206450000093
the heat storage capacity of the heat storage device at the end of t-1 time period in the cycle to be scheduledQuantity, deltahAs the dissipation factor of the thermal storage device,
Figure BDA0003348206450000094
in order to achieve the heat storage efficiency of the heat storage device,
Figure BDA0003348206450000095
for the heat release efficiency of the heat storage device, delta t is the duration of t time period in the period to be scheduled, Vt hAs shown in formula (29):
Figure BDA0003348206450000096
in the formula (I), the compound is shown in the specification,
Figure BDA0003348206450000097
is the output value M of the heating capacity of the ith heat pump in the heat pump unit in the t periodHPThe total number of heat pumps in the heat pump unit,
Figure BDA0003348206450000098
a heat load demand value for the energy-consuming equipment at the time period t;
in formula (29)
Figure BDA0003348206450000099
As in equation (30):
Figure BDA00033482064500000910
in the formula (I), the compound is shown in the specification,
Figure BDA00033482064500000911
the input power of the heating of the ith heat pump in the heat pump unit in the t period,
Figure BDA00033482064500000912
the heating coefficient of the ith heat pump in the heat pump unit in the t period is determined according to the working condition parameters of the ith heat pump.
The state transfer equation corresponding to the cold accumulation device is shown as the formula (31):
Figure BDA00033482064500000913
in the formula (I), the compound is shown in the specification,
Figure BDA00033482064500000914
the cold storage capacity of the cold storage device at the end of the t period in the period to be scheduled,
Figure BDA00033482064500000915
is the cold accumulation capacity delta of the cold accumulation device at the end of the t-1 time period in the period to be scheduledcIn order to obtain the dissipation coefficient of the cold accumulation device,
Figure BDA00033482064500000916
in order to achieve the cold storage efficiency of the cold storage device,
Figure BDA00033482064500000917
for the cold release efficiency of the cold storage device, delta t is the duration of t time period in the period to be scheduled, Vt cAs in equation (32):
Figure BDA00033482064500000918
in the formula (I), the compound is shown in the specification,
Figure BDA00033482064500000919
is the output value M of the ith heat pump refrigerating capacity in the heat pump unit in the t periodHPThe total number of heat pumps in the heat pump unit,
Figure BDA00033482064500000920
the cold load demand value of the energy consuming device for the period t.
In the formula (32)
Figure BDA00033482064500000921
Is determined according to equation (33):
Figure BDA00033482064500000922
in the formula (I), the compound is shown in the specification,
Figure BDA00033482064500000923
is the refrigeration input power of the ith heat pump in the heat pump unit in the t period,
Figure BDA00033482064500000924
the refrigeration coefficient of the ith heat pump in the heat pump unit in the t period is determined according to the working condition parameters of the ith heat pump.
When a ground source heat pump exists in the heat pump, the state transfer equation of the dynamic programming model further comprises a state transfer equation corresponding to the ground source heat pump;
the corresponding state transition equation of the ground source heat pump is as the formula (34):
Figure BDA0003348206450000101
in the formula (I), the compound is shown in the specification,
Figure BDA0003348206450000102
the total amount of heat extracted from the soil or released to the soil by the ground source heat pump at the end of the t period in the period to be scheduled,
Figure BDA0003348206450000103
the total amount of heat extracted from the soil or released to the soil by the ground source heat pump at the end of the t-1 time period in the period to be scheduled,
Figure BDA0003348206450000104
for the output value of the heating capacity of the ground source heat pump in the period t,
Figure BDA0003348206450000105
is the output value, COP, of the refrigerating capacity of the ground source heat pump in the t periodt GHP,hThe heating coefficient, COP, of the ground source heat pump in the t periodt GHP,cThe refrigeration coefficient of the ground source heat pump in the t period is shown.
For a ground source heat pump system, besides the constraints of the above equations 1 to 25, the heat release and heat absorption of the soil in the cold season and the heat supply season should be ensured to be dynamically balanced, so as to avoid the problem of underground soil heat imbalance. Thus, the optimal power plan constraint, i.e., in equation (34), should be satisfied for the total power output of the source heat pump over the period to be scheduled
Figure BDA0003348206450000106
And the constraint conditions to be satisfied are as in formula (35):
Figure BDA0003348206450000107
Figure BDA0003348206450000108
in the formula (I), the compound is shown in the specification,
Figure BDA0003348206450000109
and
Figure BDA00033482064500001010
respectively taking the total amount of heat extraction (heating) from the soil or heat release (refrigeration) to the soil for the scheduling day d of the ground source heat pump, and taking the T time period as the last time period T in the heating or refrigeration mode for the scheduling day d
Figure BDA00033482064500001011
In a corresponding manner, the first and second optical elements,
Figure BDA00033482064500001012
and
Figure BDA00033482064500001013
the output lower limit adjustment coefficient and the output upper limit adjustment coefficient on the dispatching day d when the ground source heat pump heats,
Figure BDA00033482064500001014
and
Figure BDA00033482064500001015
respectively an output lower limit adjusting coefficient and an upper limit adjusting coefficient of the ground source heat pump on a dispatching day d during refrigeration, the coefficients can be set according to the dispatching day weather and the predicted total load demand,
Figure BDA00033482064500001016
and
Figure BDA00033482064500001017
the optimal output reference values of the ground source heat pump on the dispatching day d are respectively obtained according to the following daily output optimization model on the basis of considering all the constraints of the formulas 1 to 25 and the formula 35:
Figure BDA00033482064500001018
Figure BDA00033482064500001019
Figure BDA00033482064500001020
Figure BDA00033482064500001021
in the formula, Nh，NcRespectively ending the number of types of the remaining heat supply typical period and the cooling typical period from the period to be scheduled to the whole cooling and heat supply period (typical year);
Figure BDA0003348206450000111
respectively the remaining days corresponding to the d-th heating or cooling typical period in the interval from the period to be scheduled to the end of the whole cooling and heating period (typical year);
Figure BDA0003348206450000112
respectively integrating the heat sum and the cold quantity which are actually extracted from the soil by the ground source heat pump from the corresponding cooling and heating period to the day before the period to be scheduled; dh，DcA typical set of scheduling periods for the hot season and the cold season, respectively. The model is used for optimizing the daily output with the aim of minimizing the operation cost of the whole cooling and heating cycle (typical year), and the coefficient of performance of the heat pump can be approximated to a linear function of the ambient temperature by solving the model to improve the calculation efficiency.
Preferably, when the energy supply apparatus further includes an electrical storage device, the state transition equation of the dynamic programming model further includes a state transition equation corresponding to the electrical storage device;
the state transition equation corresponding to the electric storage device is as in equation (40):
Figure BDA0003348206450000113
in the formula, SOCtState of charge, SOC, of the electrical storage device at the end of time period t in the cycle to be scheduledt-1The state of charge of the electrical storage device at the end of the t-1 time period in the scheduling cycle,
Figure BDA0003348206450000114
in order to achieve the charging efficiency of the electrical storage device,
Figure BDA0003348206450000115
for the discharge efficiency of the accumulator device, ErRated capacity of the accumulator device, Vt eAs in equation (41):
Figure BDA0003348206450000116
in the formula (I), the compound is shown in the specification,
Figure BDA0003348206450000117
and
Figure BDA0003348206450000118
the interactive power of an external power grid connected with the electric storage device, the photovoltaic power generation power and the fan power generation power are respectively in the time period t,
Figure BDA0003348206450000119
for the electrical load demand value of the energy consuming device for the period t,
Figure BDA00033482064500001110
is the input power of the ith heat pump in the heat pump unit in the t period, MHPThe total number of heat pumps in the heat pump unit.
When the energy storage device and the heat pump comprise a ground source heat pump, the state variables in the dynamic programming model select the energy storage state of the energy storage device at the end of each time period and the daily output accumulated state of the ground source heat pump, and represent that:
Figure BDA00033482064500001111
Figure BDA00033482064500001112
the decision variables select the controllable power supply and the cold and heat source output and the interaction power of the microgrid and the external network in each time period, and are represented as follows:
Figure BDA00033482064500001113
because the state variable can be valued in a continuous interval, the state variable space needs to be discretized when dynamic programming is applied, and in order to improve the optimization effect near the near-optimal solution, the method discretizes the state variable space based on the normal distribution random sequence to use the state variable
Figure BDA00033482064500001114
For example, given a mean μ e (0,1), the interval [0,1 ] is defined by the central limit theorem]In generating K random numbers xk～N(μ,σ2) σ suggests 0.4. If the number of generation is in the interval [0,1 ] in the generation process]Otherwise, the data is discarded until the number in the interval reaches K. However, the device is not suitable for use in a kitchenThen generating K states by equation (42)
Figure BDA00033482064500001115
I.e. the available set of the state variable space, and the other state variables are treated the same way.
Figure BDA0003348206450000121
And 3, optimizing the dynamic planning model by using a genetic algorithm, and determining a scheduling scheme of the comprehensive energy system.
The steps of the genetic algorithm based on dynamic programming include:
step 1: initializing state discrete number K and initial state X of comprehensive energy dynamic planning model0Initial population number, maximum iteration number, selection probability and variation probability of the genetic algorithm; the optimization variables of the genetic algorithm comprise discretization mean values (corresponding to) of state variables of T time intervals
Figure BDA0003348206450000122
) And T ═ T time period state variable values
Figure BDA0003348206450000123
Step 2: decimal coding is carried out on all optimizing variables, and an initial population is generated randomly under the constraint of upper and lower limits of the value of each variable;
and step 3: discretizing the state variable space of each stage based on a normal distribution random sequence aiming at the discretization mean value corresponding to each individual in the population, solving a scheduling problem based on dynamic programming in a corresponding available set, and recording an obtained optimization scheme U if a solution existst，XtAnd corresponding JT(XT)；
And 4, step 4: comparing J corresponding to each individualT(XT) Storing the optimal solution and the corresponding optimal scheduling scheme, and updating the discretization mean value of the individual obtaining the optimal scheme
Figure BDA0003348206450000124
Wherein alpha is random number, alpha belongs to [0,1 ]]，
Figure BDA0003348206450000125
For optimizing scheme state variable XtThe corresponding original random number takes a value.
And 5: performing a select, crossover, or mutation operation;
step 6: judging whether a termination condition is reached, if so, outputting an optimal solution and a corresponding optimal scheduling scheme; if not, return to step 3.
In step 3, the step of solving the scheduling problem based on dynamic programming is as follows:
step 3-1: t is 1, J0(X0)＝0；
Step 3-2: for each X in the t-phase state variable space available settSolving X by adopting a method of preferentially distributing energy efficiency from high to low under the condition of meeting the state transition equation and the operation constraint of the comprehensive energy systemt-1→XtCorresponding to
Figure BDA0003348206450000126
If it is not possible to find a feasible one
Figure BDA0003348206450000127
And
Figure BDA0003348206450000128
setting the value to a maximum number M;
step 3-3: calculating J according to a recurrence formulat(Xt)；
Step 3-4: if t<If T is T +1, returning to the step 3-2; otherwise output JT(XT) And corresponding optimization scheme UtAnd Xt。
The second aspect of the disclosure discloses an optimized scheduling device of an integrated energy system, which has a structure shown in fig. 2 and includes an information obtaining module 101, a model building module 102 and a scheduling scheme determining module 103.
The information acquisition module 101 is used for acquiring performance parameters, hourly operation parameters and hourly demand values of energy utilization equipment of the energy supply equipment in the comprehensive energy system in a period to be scheduled;
the model construction module 102 is configured to construct a dynamic programming model, a decision optimization objective of the dynamic programming model is that a total operation cost in each time period in a scheduling cycle is minimum, and a state transition equation of the dynamic programming model is determined according to a performance parameter, a time-by-time operation parameter and a time-by-time demand value;
the scheduling scheme determining module 103 is configured to optimize the dynamic planning model and determine a scheduling scheme of the integrated energy system.
The method and the device fully consider the change of the performance parameters of the heat pump along with the operation condition and the coupling utilization of photovoltaic, heat pump, energy storage and the like, and are closer to the actual situation. When the ground source heat pump exists, cold-heat balance operation constraint of the ground source heat pump is fully considered, the best output plan is solved through the ground source heat pump daily output optimization model, the best output plan constraint of the ground source heat pump is established, and the problem of underground soil heat imbalance is avoided. Furthermore, the invention provides a genetic algorithm based on dynamic programming to solve the comprehensive energy scheduling problem of multi-heat-pump coupling, gives consideration to the global optimization and rapidity of the scheduling problem solution, can reduce the system operation cost, and better guides the economic, efficient and environment-friendly operation of the multi-heat-pump coupling comprehensive energy system.

Claims (10)

1. An optimized scheduling method of an integrated energy system is characterized by comprising the following steps:
acquiring performance parameters, time-by-time operation parameters and time-by-time required values of energy utilization equipment in the comprehensive energy system in a period to be scheduled;
constructing a dynamic programming model, wherein the decision optimization objective of the dynamic programming model is that the total operation cost of each time interval in a scheduling cycle is minimum, and a state transition equation of the dynamic programming model is determined according to the performance parameters, the time-by-time operation parameters and the time-by-time demand values;
optimizing the dynamic planning model and determining a scheduling scheme of the comprehensive energy system.
2. The method of claim 1, wherein the energy supply device comprises a thermal storage device, a cold storage device, and a heat pump unit;
the heat storage device and the cold storage device are both connected with a heat pump unit;
the heat pump unit comprises a plurality of heat pumps, and the heat pump is at least one of an air source heat pump, a water source heat pump and a ground source heat pump.
3. The method of claim 2, wherein the state transition equations of the dynamic programming model include state transition equations for a thermal storage device and state transition equations for a cold storage device;
the state transition equation corresponding to the heat storage device is as a first formula, and the first formula is as follows:
Figure FDA0003348206440000011
in the formula (I), the compound is shown in the specification,
Figure FDA0003348206440000012
the heat storage capacity of the heat storage device at the end of the t period in the cycle to be scheduled,
Figure FDA0003348206440000013
the heat storage capacity delta of the heat storage device at the end of the t-1 time period in the cycle to be scheduledhAs the dissipation factor of the thermal storage device,
Figure FDA0003348206440000014
in order to achieve the heat storage efficiency of the heat storage device,
Figure FDA0003348206440000015
for the heat release efficiency of the heat storage device, delta t is the duration of t time period in the period to be scheduled, Vt hDetermining according to a second formula, the second formula being:
Figure FDA0003348206440000016
in the formula (I), the compound is shown in the specification,
Figure FDA0003348206440000017
is the output value M of the heating capacity of the ith heat pump in the heat pump unit in the t periodHPThe total number of heat pumps in the heat pump unit,
Figure FDA0003348206440000018
a heat load demand value for the energy-consuming equipment at the time period t;
the state transition equation corresponding to the cold accumulation device is as a third formula, and the third formula is as follows:
Figure FDA0003348206440000019
in the formula (I), the compound is shown in the specification,
Figure FDA0003348206440000021
the cold storage capacity of the cold storage device at the end of the t period in the period to be scheduled,
Figure FDA0003348206440000022
is the cold accumulation capacity delta of the cold accumulation device at the end of the t-1 time period in the period to be scheduledcIn order to obtain the dissipation coefficient of the cold accumulation device,
Figure FDA0003348206440000023
in order to achieve the cold storage efficiency of the cold storage device,
Figure FDA0003348206440000024
for the cold release efficiency of the cold storage device, delta t is the duration of t time period in the period to be scheduled, Vt cDetermined according to a fourth formula, which is:
Figure FDA0003348206440000025
in the formula (I), the compound is shown in the specification,
Figure FDA0003348206440000026
is the output value M of the ith heat pump refrigerating capacity in the heat pump unit in the t periodHPThe total number of heat pumps in the heat pump unit,
Figure FDA0003348206440000027
the cold load demand value of the energy consuming device for the period t.
4. The method of claim 3, wherein said step of determining is performed by a computer
Figure FDA0003348206440000028
Is determined according to a fifth formula, which is:
Figure FDA0003348206440000029
in the formula (I), the compound is shown in the specification,
Figure FDA00033482064400000210
the input power of the heating of the ith heat pump in the heat pump unit in the t period,
Figure FDA00033482064400000211
the method comprises the steps that the heating coefficient of the ith heat pump in a heat pump unit in a t-period is determined according to working condition parameters of the ith heat pump;
the above-mentioned
Figure FDA00033482064400000212
Is determined according to a sixth formula, said sixth formula being:
Figure FDA00033482064400000213
in the formula (I), the compound is shown in the specification,
Figure FDA00033482064400000214
is the refrigeration input power of the ith heat pump in the heat pump unit in the t period,
Figure FDA00033482064400000215
the method is characterized in that the refrigeration coefficient of the ith heat pump in the heat pump unit in the t period is determined according to the working condition parameter of the ith heat pump.
5. The method of claim 3, wherein the state transition equations of the dynamic programming model further include state transition equations corresponding to ground source heat pumps when there are ground source heat pumps in the heat pumps;
the state transition equation corresponding to the ground source heat pump is determined according to a seventh formula, wherein the seventh formula is as follows:
Figure FDA00033482064400000216
in the formula (I), the compound is shown in the specification,
Figure FDA00033482064400000217
the total amount of heat extracted from the soil or released to the soil by the ground source heat pump at the end of the t period in the period to be scheduled,
Figure FDA00033482064400000218
the total amount of heat extracted from the soil or released to the soil by the ground source heat pump at the end of the t-1 time period in the period to be scheduled,
Figure FDA00033482064400000219
for the output value of the heating capacity of the ground source heat pump in the period t,
Figure FDA0003348206440000031
is the output value of the refrigerating capacity of the ground source heat pump in the period t,
Figure FDA0003348206440000032
the heating coefficient of the ground source heat pump in the period t,
Figure FDA0003348206440000033
the refrigeration coefficient of the ground source heat pump in the t period is shown.
6. The method of claim 5, wherein said step of determining is performed by a computer
Figure FDA0003348206440000034
The constraint condition to be satisfied is an eighth formula, where the eighth formula is:
Figure FDA0003348206440000035
Figure FDA0003348206440000036
in the formula (I), the compound is shown in the specification,
Figure FDA0003348206440000037
and
Figure FDA0003348206440000038
respectively is the optimal output reference value of the ground source heat pump on the dispatching day d,
Figure FDA0003348206440000039
and
Figure FDA00033482064400000310
the output lower limit adjustment coefficient and the output upper limit adjustment coefficient on the dispatching day d when the ground source heat pump heats,
Figure FDA00033482064400000311
and
Figure FDA00033482064400000312
respectively is the output lower limit adjusting coefficient and the upper limit adjusting coefficient of the ground source heat pump on the dispatching day d during refrigeration,
Figure FDA00033482064400000313
and
Figure FDA00033482064400000314
respectively taking the total quantity of heat extracted from the soil or released into the soil for the scheduling day d of the ground source heat pump, and taking the T time period as the last time period T in the heating or cooling mode for the scheduling day d
Figure FDA00033482064400000315
Corresponding form.
7. The method of claim 5, wherein the energy supply device further comprises an electrical storage device, and accordingly, the state transition equations of the dynamic programming model further comprise state transition equations corresponding to the electrical storage device;
the state transition equation corresponding to the power storage device is a ninth formula, and the ninth formula is as follows:
Figure FDA00033482064400000316
in the formula, SOCtState of charge, SOC, of the electrical storage device at the end of time period t in the cycle to be scheduledt-1The state of charge of the electrical storage device at the end of the t-1 time period in the scheduling cycle,
Figure FDA00033482064400000317
in order to achieve the charging efficiency of the electrical storage device,
Figure FDA00033482064400000318
for the discharge efficiency of the accumulator device, ErRated capacity of the accumulator device, Vt eDetermined according to a tenth formula, which is:
Figure FDA00033482064400000319
in the formula (I), the compound is shown in the specification,
Figure FDA00033482064400000320
and
Figure FDA00033482064400000321
the interactive power, the photovoltaic power generation power and the fan power generation power of an external power grid connected with the power storage device in the time period t are respectively,
Figure FDA00033482064400000322
for the electrical load demand value of the energy consuming device for the period t,
Figure FDA00033482064400000323
is the input power of the ith heat pump in the heat pump unit in the t period, MHPThe total number of heat pumps in the heat pump unit.
8. A method according to any of claims 1-7, characterized in that the decision-making optimization objective of the dynamic programming model is as an eleventh formula:
Figure FDA0003348206440000041
in the formula (I), the compound is shown in the specification,
Figure FDA0003348206440000042
maintenance cost for power supply output of ith power supply equipment unit，
Figure FDA0003348206440000043
For the power supply output, Ep, of the ith power supply device during the period ttIs the electricity rate for the period of time t,
Figure FDA0003348206440000044
is the electricity purchasing quantity in the period of t, XtIs a decision variable and
Figure FDA0003348206440000045
Figure FDA0003348206440000046
for the ground source heat pump to extract the total heat from the soil or release the total heat to the soil in the period to be scheduled,
Figure FDA0003348206440000047
the heat storage capacity of the heat storage device at the end of the t period in the cycle to be scheduled,
Figure FDA0003348206440000048
for the cold storage capacity, SOC, of the cold storage device at the end of the t period in the period to be scheduledtAnd the charge state of the power storage device at the end of the t period in the period to be scheduled.
9. The method of claim 1, wherein optimizing the dynamic programming model comprises:
and optimizing the dynamic programming model by utilizing a genetic algorithm.
10. An integrated energy system optimization scheduling device is characterized by comprising:
the information acquisition module is used for acquiring performance parameters, time-by-time operation parameters and time-by-time required values of energy utilization equipment in the comprehensive energy system in a period to be scheduled;
the model building module is used for building a dynamic planning model, the decision optimization goal of the dynamic planning model is that the total operation cost of each time interval in a scheduling cycle is minimum, and a state transition equation of the dynamic planning model is determined according to the performance parameters, the time-by-time operation parameters and the time-by-time demand values;
and the scheduling scheme determining module is used for optimizing the dynamic planning model and determining the scheduling scheme of the comprehensive energy system.
CN202111329448.4A 2021-11-10 2021-11-10 Optimized scheduling method and device for comprehensive energy system Pending CN113902339A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114719320A (en) * 2022-04-20 2022-07-08 国网河北能源技术服务有限公司 Dispatching method and device of multiple heat pump systems and terminal equipment
CN116663870A (en) * 2023-08-02 2023-08-29 北京世纪黄龙技术有限公司 Heat supply system scheduling method and system based on cloud computing

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114719320A (en) * 2022-04-20 2022-07-08 国网河北能源技术服务有限公司 Dispatching method and device of multiple heat pump systems and terminal equipment
CN114719320B (en) * 2022-04-20 2024-03-29 国网河北能源技术服务有限公司 Scheduling method and device of multi-heat pump system and terminal equipment
CN116663870A (en) * 2023-08-02 2023-08-29 北京世纪黄龙技术有限公司 Heat supply system scheduling method and system based on cloud computing
CN116663870B (en) * 2023-08-02 2023-10-03 北京世纪黄龙技术有限公司 Heat supply system scheduling method and system based on cloud computing

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