CN114358431A - Multi-energy system optimal scheduling method and device considering supply and demand bidirectional demand response - Google Patents

Multi-energy system optimal scheduling method and device considering supply and demand bidirectional demand response Download PDF

Info

Publication number
CN114358431A
CN114358431A CN202210015049.9A CN202210015049A CN114358431A CN 114358431 A CN114358431 A CN 114358431A CN 202210015049 A CN202210015049 A CN 202210015049A CN 114358431 A CN114358431 A CN 114358431A
Authority
CN
China
Prior art keywords
energy
power
node
heat
demand response
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202210015049.9A
Other languages
Chinese (zh)
Inventor
董帅
苗骁健
刘宏波
菅学辉
孙丰杰
刘舜超
周生奇
李�昊
王杉
张媛
撖奥洋
于洋
钟世民
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
QINGDAO POWER SUPPLY Co OF STATE GRID SHANDONG ELECTRIC POWER Co
State Grid Corp of China SGCC
Original Assignee
QINGDAO POWER SUPPLY Co OF STATE GRID SHANDONG ELECTRIC POWER Co
State Grid Corp of China SGCC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by QINGDAO POWER SUPPLY Co OF STATE GRID SHANDONG ELECTRIC POWER Co, State Grid Corp of China SGCC filed Critical QINGDAO POWER SUPPLY Co OF STATE GRID SHANDONG ELECTRIC POWER Co
Priority to CN202210015049.9A priority Critical patent/CN114358431A/en
Publication of CN114358431A publication Critical patent/CN114358431A/en
Pending legal-status Critical Current

Links

Images

Landscapes

  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a multi-energy system optimal scheduling method considering supply and demand bidirectional demand response, which comprises the following steps: determining and acquiring basic operation parameters of the comprehensive energy system; modeling an integrated energy system considering the electricity-heat multi-energy flow; constructing an energy utilization side comprehensive demand response model considering electric load and thermal load; constructing an energy supply side demand response model considering wind power, electric hydrogen production and a hydrogen storage tank; constructing a target function with the lowest energy consumption satisfaction degree and operation cost of the comprehensive energy system; constructing constraint conditions of energy balance constraint, system network constraint and equipment model constraint; and solving to obtain a day-ahead optimized scheduling scheme of the comprehensive energy system. The method can realize the balance of supply and demand and the economic operation of the comprehensive energy system through multi-energy flow complementary coordination on the premise of meeting the requirements of electric load and heat load.

Description

Multi-energy system optimal scheduling method and device considering supply and demand bidirectional demand response
Technical Field
The invention relates to an optimized scheduling scheme of a comprehensive energy system, in particular to a method and a device for optimized scheduling of a multi-energy system considering supply and demand bidirectional demand response.
Background
The energy internet is a new generation energy system which takes an electric power system as a core and a link and constructs the internet of various types of energy, and realizes multi-energy complementary cooperation, cleanness, low carbon, safety and high efficiency. The realization of the deep fusion of an energy system and the Internet becomes an important energy strategic demand and a related industrial development trend in China. The dynamic response characteristic of the resources is mastered, and the method plays an important role in exerting the flexible regulation effect of the resources and promoting the safe and stable operation of the energy Internet. In the prior art, the demand response of an energy utilization side is considered more, the consideration on the response characteristic of the energy supply side is less, and the function of comprehensively considering the supply and demand bidirectional demand response characteristic in improving the optimization scheduling flexibility and the economy of the multi-energy system is neglected.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a multi-energy system optimization scheduling method considering supply and demand bidirectional demand response. According to the method, a comprehensive energy system optimization scheduling model considering supply and demand bidirectional demand response is constructed through energy equipment such as a cogeneration unit, a heat pump, a gas turbine, an electric hydrogen production tank and a hydrogen storage tank, multi-energy flow complementation and complementation can be realized, and the energy supply diversity and the overall economy of the system are improved.
In order to solve the technical problem, the invention adopts the following technical scheme:
the optimal scheduling method of the multi-energy system considering supply and demand bidirectional demand response comprises the following steps:
determining and acquiring basic operation parameters of the comprehensive energy system;
constructing a comprehensive energy system model considering the electricity-heat multi-energy flow;
constructing an energy utilization side comprehensive demand response model considering electric load and thermal load;
constructing an energy supply side demand response model considering wind power, electric hydrogen production and a hydrogen storage tank;
constructing a target function with the lowest energy consumption satisfaction degree and operation cost of the comprehensive energy system;
acquiring constraint conditions of energy balance constraint, system network constraint and equipment model constraint;
and solving the objective function to obtain a day-ahead optimized scheduling scheme of the comprehensive energy system.
Further, the method for constructing the comprehensive energy system model considering the electricity-heat multi-energy flow comprises the following steps:
carrying out power grid modeling on the radial power distribution network: the method comprises the following steps of establishing a network model of the power distribution network by adopting a Dist-Flow power Flow equation, wherein the network model comprises an active power Flow equation, a reactive power Flow equation and a voltage equation, namely:
Figure BDA0003460126930000011
Figure BDA0003460126930000021
Figure BDA0003460126930000022
wherein, Pi,tAnd Qi,tRespectively the active power and the reactive power on the line from the power grid node i to the node i +1 at the moment t; r isiThe resistance of the line from the node i to the node i +1 at the time t;
Figure BDA0003460126930000023
and
Figure BDA0003460126930000024
respectively an active load at a power grid node i at the moment t and active power of a power supply; x is the number ofiThe reactance of the line from node i to node i +1 at time t;
Figure BDA0003460126930000025
and
Figure BDA0003460126930000026
respectively the reactive load and the power supply reactive power at a grid node i at the time t; vi,tIs the voltage at node i at time t;
introducing variables shown in the formula (4), and performing second-order cone relaxation on the distribution network model to obtain a formula (5):
Figure BDA0003460126930000027
Figure BDA0003460126930000028
and (3) modeling a heat supply network: the heat variation relationship of the pipe ij can be expressed as:
Figure BDA0003460126930000029
in the formula, Hi,tAnd Hj,tRespectively the heat energy flow at the node i and the node j at the time t in the heat supply network system; l isijIs the length of conduit ij; sigma RunitThe thermal resistance of the unit length from the heating medium to the environment medium; t isij，tThe temperature of a heating medium of a pipeline ij at the moment t in the heat supply network system; t isa,tIs the ambient temperature around the pipeline; c is the specific heat capacity of water; m isi,tAnd mj,tRespectively are heat medium mass flows at the node i and the node j; t isi,tAnd Tj,tRespectively the temperature of the heating medium at the node i and the node j;
the pipe end temperature, due to the pipe heat transfer losses due to the temperature difference between the pipe and the surrounding environment, can be expressed as:
Figure BDA00034601269300000210
Figure BDA00034601269300000211
wherein alpha isijIs the loss constant of the pipe ij; k is a radical ofijIs the heat loss coefficient of the pipe ij; m isij，tIs the heat medium mass flow of the pipe ij at the time t in the heat supply network system.
Further, the method for constructing the energy utilization side comprehensive demand response model considering the electric load and the thermal load comprises the following steps:
modeling the electric load demand response:
Figure BDA0003460126930000031
Figure BDA0003460126930000032
Figure BDA0003460126930000033
wherein the content of the first and second substances,
Figure BDA0003460126930000034
and
Figure BDA0003460126930000035
respectively representing the fixed electric load, the transferable electric load and the total electric load at the t moment at the power grid node i;
Figure BDA0003460126930000036
representing the upper limit of the transferable electrical load at grid node i;
Figure BDA0003460126930000037
representing the total transferable electric load in a dispatching cycle at a power grid node i;
modeling of thermal load demand response:
Figure BDA0003460126930000038
Figure BDA0003460126930000039
Figure BDA00034601269300000310
wherein the content of the first and second substances,
Figure BDA00034601269300000311
and
Figure BDA00034601269300000312
respectively represents the fixed heat load and the transferable heat load at the heat supply network node k at the moment tLoad and total heat load;
Figure BDA00034601269300000313
represents an upper bound for the transferable thermal load at heat grid node k;
Figure BDA00034601269300000314
representing the total amount of transferable thermal load during the scheduling period at heat network node k.
Further, an energy supply side demand response model considering wind power, electric hydrogen production and a hydrogen storage tank is constructed, and the method comprises the following steps:
constructing an electric hydrogen production and storage tank model:
Figure BDA00034601269300000315
Figure BDA00034601269300000316
Figure BDA00034601269300000317
Figure BDA00034601269300000318
Figure BDA00034601269300000319
Figure BDA00034601269300000320
Ich,t,i+Idis,t,i≤1 (21)
wherein the content of the first and second substances,
Figure BDA00034601269300000321
and H2t,iRespectively the power consumption power and the hydrogen production power of the electrolytic cell at the power grid node i at the time t; etaiRepresenting the hydrogen production efficiency of the electrolytic cell at the power grid node i;
Figure BDA00034601269300000322
representing the maximum power consumption of the electrolytic cell at the node i of the power grid;
Figure BDA00034601269300000323
and
Figure BDA00034601269300000324
maximum power of hydrogen charging and discharging of a hydrogen storage tank at a power grid node i is respectively set;
Figure BDA00034601269300000325
and
Figure BDA00034601269300000326
the power of charging and discharging hydrogen of the hydrogen storage tank at the power grid node i at the moment t is respectively;
Figure BDA00034601269300000327
and
Figure BDA00034601269300000328
the hydrogen storage amounts of the hydrogen storage tank at t-1 and t moments at the power grid node i are respectively;
Figure BDA00034601269300000329
respectively representing the minimum value and the maximum value of the hydrogen storage capacity of the hydrogen storage tank at the power grid node i; alpha is alphap2h,iThe loss rate of the hydrogen storage tank at the power grid node i is obtained; etach,iAnd ηdis,iRespectively representing the hydrogen energy storage efficiency and the hydrogen energy release efficiency of a hydrogen storage tank at a power grid node i; i isch,t,iAnd Idis,t,iThe energy storage state is a variable of 0-1 when the hydrogen storage tank at the power grid node i is in an energy storage or release state at the moment t;
and (3) wind power scheduling constraint condition formulation:
Figure BDA0003460126930000041
Figure BDA0003460126930000042
Figure BDA0003460126930000043
wherein the content of the first and second substances,
Figure BDA0003460126930000044
representing the predicted wind power at the t moment of a power grid node i;
Figure BDA0003460126930000045
representing the actual wind power at time t at grid node i,
Figure BDA0003460126930000046
and
Figure BDA0003460126930000047
and respectively representing the wind power parts participating in hydrogen production by electricity and system scheduling at the time t at the power grid node i.
Further, the method for constructing the objective function with the lowest energy utilization satisfaction and operation cost of the comprehensive energy system comprises the following steps:
and (3) making an operation cost objective function:
for the type of unit involved, the operating costs include the cost of powering the equipment and the cost of purchasing and selling electricity:
Figure BDA0003460126930000048
wherein, CenergyThe cost of energy supply to the system is reduced,
Figure BDA0003460126930000049
for a time t period of n equipment operating costs of the unit, wherein m3＝{GT,GB, CHP, WT, ESS, TES, HES }, GT, GB, WT, CHP, ESS, TES, HES are respectively a gas turbine, a gas boiler, a wind power and cogeneration unit, an electric energy storage tank, a heat storage tank and a hydrogen storage tank; n is a radical ofm3The total number of the energy supply units;
Figure BDA00034601269300000410
the electricity purchasing and selling cost is t time period;
Figure BDA00034601269300000411
is the hydrogen purchase cost for the time period t;
Figure BDA00034601269300000412
wherein the content of the first and second substances,
Figure BDA00034601269300000413
the t time period and the electricity purchasing power and the electricity selling power of the superior power grid are respectively,
Figure BDA00034601269300000414
respectively obtaining the electricity purchasing price and the electricity selling price at the time t;
Figure BDA00034601269300000415
wherein p ist、λtHydrogen purchasing power and hydrogen purchasing price in a time period t are respectively;
and (3) setting a wind abandon punishment cost:
Figure BDA00034601269300000416
wherein, CpunisRepresenting wind curtailment penalty cost; c. CwtRepresenting a wind curtailment penalty coefficient;
the user uses an energy satisfaction target function to make:
Figure BDA00034601269300000417
wherein, CdrRepresenting wind curtailment penalty cost; v. ofe、αeTo use the electrical preference coefficient, vh、αhIs the thermal preference coefficient;
and (3) making a comprehensive optimization objective function:
minF＝min(Cenergy+Cpunis+Cdr) (30)
wherein F is the comprehensive cost of the system.
Furthermore, the equipment model constraint comprises unit output upper and lower limit constraint, unit climbing constraint, energy storage equipment constraint and extraction condensing type thermoelectric unit energy conversion constraint.
Further, a commercial solver CPLEX is used for solving the comprehensive energy system model.
The multi-energy system optimization scheduling device considering supply and demand bidirectional demand response comprises:
the parameter acquisition module is used for determining and acquiring basic operation parameters of the comprehensive energy system;
the comprehensive energy system model building module is used for building a comprehensive energy system model considering electricity-heat multi-energy flow;
the energy utilization side comprehensive demand response model building module is used for building an energy utilization side comprehensive demand response model considering electric load and heat load;
the energy supply side demand response model building module is used for building an energy supply side demand response model considering wind power, electric hydrogen production and a hydrogen storage tank;
the objective function construction module is used for constructing an objective function with the lowest energy utilization satisfaction degree and operation cost of the comprehensive energy system;
the constraint condition acquisition module is used for acquiring constraint conditions of energy balance constraint, system network constraint and equipment model constraint;
and the objective function solving module is used for solving the objective function to obtain the day-ahead optimized scheduling scheme of the comprehensive energy system.
A computing device, comprising: one or more processing units; and the storage unit is used for storing one or more programs, and when the one or more programs are executed by the one or more processing units, the one or more processing units are enabled to execute the multi-energy system optimization scheduling method considering the supply and demand bidirectional demand response.
A computer readable storage medium having non-transitory program code executable by a processor, the computer program when executed by the processor implementing the steps of the multi-energy system optimization scheduling method taking into account supply and demand bi-directional demand responses as described above.
Compared with the prior art, the scheme adopts the multi-energy system optimized dispatching method considering supply and demand bidirectional demand response, and achieves the following beneficial effects:
(1) the optimized scheduling method can promote multi-energy flow complementary coordination on the premise of meeting the requirements of electric load, heat load and hydrogen load, and realize the balance of supply and demand and economic operation of a comprehensive energy system;
(2) the optimized dispatching method realizes peak clipping and valley filling of the electric load and the thermal load by using the energy side comprehensive demand response, simultaneously introduces the electrolytic cell and the hydrogen storage tank, considers the energy supply side demand response, increases energy supply diversity for the comprehensive energy system, optimizes the wind power dispatching strategy and improves the wind power consumption level.
Drawings
FIG. 1 is a flow chart of a multi-energy system optimization scheduling method considering supply and demand bidirectional demand response;
FIG. 2 is a network diagram of an integrated energy system according to an embodiment of the present invention;
FIG. 3 is a load and wind power output curve diagram according to an embodiment of the present invention;
FIG. 4 is a comparison of electrical load demand response before and after an embodiment of the present invention;
FIG. 5 is a comparison of thermal load demand response before and after an embodiment of the present invention;
FIG. 6 is a comparison of wind power consumption under different scenarios according to the embodiment of the present invention;
FIG. 7 is a diagram illustrating a wind power dispatching scenario according to an embodiment of the present invention;
FIG. 8 is a diagram illustrating scheduling power balance optimization results according to an embodiment of the present invention;
FIG. 9 is a diagram illustrating scheduling thermodynamic equilibrium optimization results according to an embodiment of the present invention;
fig. 10 shows the result of optimizing hydrogen energy balance in scheduling according to the embodiment of the present invention.
Detailed Description
The invention provides a multi-energy system optimal scheduling method considering supply and demand bidirectional demand response. Firstly, constructing an electric-thermal multi-energy-flow comprehensive energy system model; then, respectively constructing an energy utilization side comprehensive demand response model considering the electric load and the heat load and an energy supply side demand response model considering the wind power, the electric hydrogen production and the hydrogen storage tank; and finally, taking the lowest operation cost and the energy utilization satisfaction degree of the comprehensive energy system as an objective function and taking the energy balance constraint and the network constraint as constraint conditions, and providing a day-ahead optimization scheduling scheme of the comprehensive energy system. The example simulation result shows that the optimized scheduling method can realize the balance of supply and demand and the economic operation of the comprehensive energy system through multi-energy flow complementary coordination on the premise of meeting the requirements of electric load and heat load.
The optimal scheduling method of the multi-energy system considering supply and demand bidirectional demand response comprises the following steps:
determining and acquiring basic operation parameters of the comprehensive energy system;
constructing a comprehensive energy system model considering the electricity-heat multi-energy flow;
constructing an energy utilization side comprehensive demand response model considering electric load and thermal load;
constructing an energy supply side demand response model considering wind power, electric hydrogen production and a hydrogen storage tank;
constructing a target function with the lowest energy consumption satisfaction degree and operation cost of the comprehensive energy system;
acquiring constraint conditions of energy balance constraint, system network constraint and equipment model constraint;
and solving the objective function to obtain a day-ahead optimized scheduling scheme of the comprehensive energy system.
Further, the method for constructing the comprehensive energy system model considering the electricity-heat multi-energy flow comprises the following steps:
carrying out power grid modeling on the radial power distribution network: the method comprises the following steps of establishing a network model of the power distribution network by adopting a Dist-Flow power Flow equation, wherein the network model comprises an active power Flow equation, a reactive power Flow equation and a voltage equation, namely:
Figure BDA0003460126930000071
Figure BDA0003460126930000072
Figure BDA0003460126930000073
wherein, Pi,tAnd Qi,tRespectively the active power and the reactive power on the line from the power grid node i to the node i +1 at the moment t; r isiThe resistance of the line from the node i to the node i +1 at the time t;
Figure BDA0003460126930000074
and
Figure BDA0003460126930000075
respectively an active load at a power grid node i at the moment t and active power of a power supply; x is the number ofiThe reactance of the line from node i to node i +1 at time t;
Figure BDA0003460126930000076
and
Figure BDA0003460126930000077
respectively the reactive load and the power supply reactive power at a grid node i at the time t; vi,tIs the voltage at node i at time t;
introducing variables shown in the formula (4), and performing second-order cone relaxation on the distribution network model to obtain a formula (5):
Figure BDA0003460126930000078
Figure BDA0003460126930000079
and (3) modeling a heat supply network: the heat variation relationship of the pipe ij can be expressed as:
Figure BDA00034601269300000710
in the formula, Hi,tAnd Hj,tRespectively the heat energy flow at the node i and the node j at the time t in the heat supply network system; l isijIs the length of conduit ij; sigma RunitThe thermal resistance of the unit length from the heating medium to the environment medium; t isij，tThe temperature of a heating medium of a pipeline ij at the moment t in the heat supply network system; t isa,tIs the ambient temperature around the pipeline; c is the specific heat capacity of water; m isi,tAnd mj,tRespectively are heat medium mass flows at the node i and the node j; t isi,tAnd Tj,tRespectively the temperature of the heating medium at the node i and the node j;
the pipe end temperature, due to the pipe heat transfer losses due to the temperature difference between the pipe and the surrounding environment, can be expressed as:
Figure BDA00034601269300000711
Figure BDA00034601269300000712
wherein alpha isijIs the loss constant of the pipe ij; k is a radical ofijIs the heat loss coefficient of the pipe ij; m isij，tIs the heat medium mass flow of the pipe ij at the time t in the heat supply network system.
Further, the method for constructing the energy utilization side comprehensive demand response model considering the electric load and the thermal load comprises the following steps:
modeling the electric load demand response:
Figure BDA0003460126930000081
Figure BDA0003460126930000082
Figure BDA0003460126930000083
wherein the content of the first and second substances,
Figure BDA0003460126930000084
and
Figure BDA0003460126930000085
respectively representing the fixed electric load, the transferable electric load and the total electric load at the t moment at the power grid node i;
Figure BDA0003460126930000086
representing the upper limit of the transferable electrical load at grid node i;
Figure BDA0003460126930000087
representing the total transferable electric load in a dispatching cycle at a power grid node i;
modeling of thermal load demand response:
Figure BDA0003460126930000088
Figure BDA0003460126930000089
Figure BDA00034601269300000810
wherein the content of the first and second substances,
Figure BDA00034601269300000811
and
Figure BDA00034601269300000812
respectively representing the fixed heat load, the transferable heat load and the total heat load at the heat supply network node k at the moment t;
Figure BDA00034601269300000813
represents an upper bound for the transferable thermal load at heat grid node k;
Figure BDA00034601269300000814
representing the total amount of transferable thermal load during the scheduling period at heat network node k.
Further, an energy supply side demand response model considering wind power, electric hydrogen production and a hydrogen storage tank is constructed, and the method comprises the following steps:
constructing an electric hydrogen production and storage tank model:
Figure BDA00034601269300000815
Figure BDA00034601269300000816
Figure BDA00034601269300000817
Figure BDA00034601269300000818
Figure BDA00034601269300000819
Figure BDA00034601269300000820
Ich,t,i+Idis,t,i≤1 (21)
wherein the content of the first and second substances,
Figure BDA00034601269300000821
and H2t,iRespectively the power consumption power and the hydrogen production power of the electrolytic cell at the power grid node i at the time t; etaiRepresenting the hydrogen production efficiency of the electrolytic cell at the power grid node i;
Figure BDA00034601269300000822
representing the maximum power consumption of the electrolytic cell at the node i of the power grid;
Figure BDA00034601269300000823
and
Figure BDA00034601269300000824
maximum power of hydrogen charging and discharging of a hydrogen storage tank at a power grid node i is respectively set;
Figure BDA00034601269300000825
and
Figure BDA00034601269300000826
the power of charging and discharging hydrogen of the hydrogen storage tank at the power grid node i at the moment t is respectively;
Figure BDA0003460126930000091
and
Figure BDA0003460126930000092
the hydrogen storage amounts of the hydrogen storage tank at t-1 and t moments at the power grid node i are respectively;
Figure BDA0003460126930000093
respectively representing the minimum value and the maximum value of the hydrogen storage capacity of the hydrogen storage tank at the power grid node i; alpha is alphap2h,iFor the loss rate of the hydrogen storage tank at the node i of the power grid；ηch,iAnd ηdis,iRespectively representing the hydrogen energy storage efficiency and the hydrogen energy release efficiency of a hydrogen storage tank at a power grid node i; i isch,t,iAnd Idis,t,iThe energy storage state is a variable of 0-1 when the hydrogen storage tank at the power grid node i is in an energy storage or release state at the moment t;
and (3) wind power scheduling constraint condition formulation:
Figure BDA0003460126930000094
Figure BDA0003460126930000095
Figure BDA0003460126930000096
wherein the content of the first and second substances,
Figure BDA0003460126930000097
representing the predicted wind power at the t moment of a power grid node i;
Figure BDA0003460126930000098
representing the actual wind power at time t at grid node i,
Figure BDA0003460126930000099
and
Figure BDA00034601269300000910
and respectively representing the wind power parts participating in hydrogen production by electricity and system scheduling at the time t at the power grid node i.
Further, the method for constructing the objective function with the lowest energy utilization satisfaction and operation cost of the comprehensive energy system comprises the following steps:
and (3) making an operation cost objective function:
for the related unit types, GT, GB, WT, CHP, ESS, TES and HES are respectively a gas turbine, a gas boiler, a wind power and cogeneration unit, an electric energy storage and heat storage tank and a hydrogen storage tank; the operating costs include equipment energy supply costs and electricity purchase and sale costs:
Figure BDA00034601269300000911
wherein, CenergyThe cost of energy supply to the system is reduced,
Figure BDA00034601269300000912
for a time t period of n equipment operating costs of the unit, wherein m3Calculating the running cost of a gas turbine, a gas boiler, wind power, a cogeneration unit, an electric energy storage tank, a heat storage tank and a hydrogen storage tank;
Figure BDA00034601269300000913
the total number of the energy supply units;
Figure BDA00034601269300000914
the electricity purchasing and selling cost is t time period;
Figure BDA00034601269300000915
is the hydrogen purchase cost for the time period t;
Figure BDA00034601269300000916
wherein the content of the first and second substances,
Figure BDA00034601269300000917
the t time period and the electricity purchasing power and the electricity selling power of the superior power grid are respectively,
Figure BDA00034601269300000918
respectively obtaining the electricity purchasing price and the electricity selling price at the time t;
Figure BDA00034601269300000919
wherein p ist、λtHydrogen purchasing power and hydrogen purchasing price in a time period t are respectively;
and (3) setting a wind abandon punishment cost:
Figure BDA00034601269300000920
wherein, CpunisRepresenting wind curtailment penalty cost; c. CwtRepresenting a wind curtailment penalty coefficient;
the user uses an energy satisfaction target function to make:
Figure BDA0003460126930000101
wherein, CdrRepresenting wind curtailment penalty cost; v. ofe、αeTo use the electrical preference coefficient, vh、αhIs the thermal preference coefficient;
and (3) making a comprehensive optimization objective function:
minF＝min(Cenergy+Cpunis+Cdr) (30)
wherein F is the comprehensive cost of the system.
Further, constraint conditions of energy balance constraint, system network constraint and equipment model constraint are constructed, and the following steps are adopted for formulation:
the system energy balance constraint is constructed as follows:
the system operation needs to meet the energy balance constraints of electricity, heat and hydrogen respectively:
Figure BDA0003460126930000102
Figure BDA0003460126930000103
Figure BDA0003460126930000104
wherein the content of the first and second substances,
Figure BDA0003460126930000105
electric power generated by a gas turbine and wind power at a grid node i in the period t respectively;
Figure BDA0003460126930000106
the thermal power of the gas turbine at a heat supply network node k in a period t;
Figure BDA0003460126930000107
electric power exchanged with an external network at a grid node i for a period t;
Figure BDA0003460126930000108
is the electrical load at grid node i for time period t;
Figure BDA0003460126930000109
the electric output and the thermal output of the CHP unit connected to the power grid node i and the heat supply network node k in the time period t are respectively;
Figure BDA00034601269300001010
the hydrogen purchasing power at the grid node i in the period t;
Figure BDA00034601269300001011
is the hydrogen load at grid node i for time period t;
Figure BDA00034601269300001012
the power of the electricity storage device and the heat storage device which are connected to the power grid node i and the heat supply network node k in the time period t respectively;
the system network constraint is constructed as follows:
and (3) power grid constraint:
Vi min≤Vi,t≤Vi max (34)
Figure BDA00034601269300001013
wherein, Vi minAnd Vi maxThe minimum value and the maximum value of the allowed voltage of the node i; vi,tIs the voltage at node i at time t; i isij,tThe current flowing on the line ij at the time t;
Figure BDA00034601269300001014
the maximum value of the current allowed to flow on line ij.
And (3) heat supply network constraint:
Figure BDA00034601269300001015
Figure BDA00034601269300001016
Figure BDA0003460126930000111
Figure BDA0003460126930000112
Figure BDA0003460126930000113
Figure BDA0003460126930000114
Figure BDA0003460126930000115
wherein the content of the first and second substances,
Figure BDA0003460126930000116
the temperature of the water supply and return is the upper and lower limits;
Figure BDA0003460126930000117
the temperature of the supply water and the return water of a node k in the period t;
Figure BDA0003460126930000118
the heat output of the heat source node and the heat exchange quantity of the heat exchange station node are obtained;
Figure BDA0003460126930000119
the mass flow of the heating medium flowing into the node k for the period t; q. q.st,kThe mass flow of the heating medium flowing into the node k for the period t;
Figure BDA00034601269300001110
a pipeline set with a node k as a head end and a tail end; t ist,kThe temperature and the lower stage pipe inlet temperature are mixed for the time period t, node k.
The equipment model constraints constructed are:
and (3) restraining the upper and lower limits of the unit output:
Figure BDA00034601269300001111
wherein the content of the first and second substances,
Figure BDA00034601269300001112
respectively an upper limit and a lower limit of the n electric output of the unit, wherein m4The output of the gas turbine, the gas boiler, the wind power generation unit and the cogeneration unit is bound by upper and lower limits of the output of the unit;
unit climbing restraint, namely:
Figure BDA00034601269300001113
wherein the content of the first and second substances,
Figure BDA00034601269300001114
and
Figure BDA00034601269300001115
n landslide and climbing rates for the unit, where m6The method comprises the following steps of (1) setting (GT, GB, CHP), namely, a gas turbine, a gas boiler and a cogeneration unit all need to meet unit climbing constraints;
energy storage device constraints, namely:
in order to ensure the consistency of scheduling, the energy storage device needs to ensure that the storage amounts at the starting and stopping moments of scheduling are the same, and the storage and release processes of energy cannot be performed simultaneously. The constraints of the electrical energy storage device are as shown in equation (45). The principle of heat energy storage is similar, and the detailed description is omitted.
Figure BDA00034601269300001116
Wherein the content of the first and second substances,
Figure BDA0003460126930000121
the charging and discharging power of the electric energy storage device is t time period; pt ESSThe electric energy storage amount of the electric energy storage device is t time period; pESS,max、PESS,minUpper and lower limits of the power storage power of the electric energy storage equipment;
Figure BDA0003460126930000122
and
Figure BDA0003460126930000123
are respectively as
Figure BDA0003460126930000124
And
Figure BDA0003460126930000125
an upper power limit of (d);
Figure BDA0003460126930000126
and
Figure BDA0003460126930000127
discharge and charge state variables, respectively.
Energy conversion constraint of the extraction condensing type thermoelectric unit, namely:
Figure BDA0003460126930000128
wherein the content of the first and second substances,
Figure BDA0003460126930000129
the minimum and maximum electric output of the unit n under the condensing working condition;
Figure BDA00034601269300001210
the upper limit of the n thermal output of the unit is set; c. Cm,n、Km,n、cv,nIs a unit constant.
Furthermore, by combining the objective function and the constraint condition, the comprehensive energy system optimization scheduling model belongs to the mixed integer nonlinear programming problem, and a commercial solver CPLEX is called to solve the model.
The scheme comprises the following contents:
(1) composition of comprehensive energy system
The system generates electric energy and heat energy through a combined heat and power generation unit (CHP); the electric heat conversion is realized by a Gas Boiler (GB). The wind power (WT), the Gas Turbine (GT) and the upper-level power grid supply electric power for electric load users together; in addition, hydrogen energy is generated by the electrolytic cell to supply energy to the hydrogen load, and electric energy storage (ESS), Thermal Energy Storage (TES) and a hydrogen storage tank (HES) can store or release energy when the energy is excessive or insufficient;
(2) thermodynamic system modeling
Different from the characteristics of small inertia and quick adjustment of a power system, the thermodynamic system has larger system inertia in scheduling, the heat loss in the heat medium transmission process has direct influence on the temperature of each part of a heat medium, and the pipeline heat transmission loss generated by the temperature difference between a pipeline and the surrounding environment is calculated in the heat loss treatment to obtain the tail end temperature of the pipeline;
(3) optimized scheduling model of comprehensive energy system
A supply and demand bidirectional demand response mechanism is introduced, the optimization target of minimum comprehensive cost of system economy and wind curtailment punishment and maximum energy utilization satisfaction is taken into consideration, and system energy balance constraint, equipment model constraint, system network constraint and the like are considered, so that the supply and demand bidirectional demand response-considering multi-energy system optimization scheduling method is provided.
The process of the multi-energy system optimization scheduling method considering supply and demand bidirectional demand response is shown in fig. 1, and the principle and the steps are as follows:
1) initializing 101, namely initializing basic parameters such as a network structure, equipment access positions and maximum power;
2) acquiring day-ahead prediction data 102 of wind power and load;
3) modeling 103 an integrated energy system that considers electric-thermal multi-energy flows;
4) constructing an energy utilization side comprehensive demand response model 104 considering electric load and thermal load;
5) constructing an energy supply side demand response model 105 considering wind power, electric hydrogen production and a hydrogen storage tank;
6) constructing a comprehensive objective function 106;
7) constructing constraint conditions 107 of energy balance constraint, system network constraint and equipment model constraint;
8) constructing an optimization problem 108;
9) solving an optimization problem 109;
10) and outputting the day-ahead optimization scheduling scheme 110 of the integrated energy system.
Example (b):
the optimized scheduling method of the multi-energy system considering supply and demand bidirectional demand response provided by the example is based on an integrated energy system example formed by an improved IEEE 33 node power distribution system and a 6 node thermodynamic system, and is shown in FIG. 2. The system comprises a cogeneration unit, a wind turbine unit, a pure gas turbine generator unit and the like. The heat supply network performs energy conversion with the power grid through the cogeneration unit and the gas boiler. The curves of the system electrical load, thermal load, hydrogen load and wind power output are shown in fig. 3. The main parameters of each apparatus are shown in table 1.
TABLE 1 Equipment parameters
Figure BDA0003460126930000131
The scheme is modeled by a YALMIP toolbox and solves the problem by Cplex12.8.0. Comparative simulation analysis was performed for category 6 scenes based on the above given, as shown in table 2. In the table, "√" and "X" indicate consideration and non-consideration of the influencing factor, respectively.
Table 2 comparison of scheduling scheme conditions
Figure BDA0003460126930000141
FIG. 4 is a comparison before and after electrical load consideration of demand response, and FIG. 5 is a comparison before and after thermal load consideration of demand response. The peak clipping and valley filling of the electric load and the heat load are realized by the electric heating comprehensive demand response of the energy side. Fig. 6 shows the comparison of the wind power consumption of the system in 6 scenarios, and the wind power consumption proportion of the integrated energy system can be improved to different degrees by considering the demand response of the electric load, the demand response of the heat load and the demand response of the energy supply side.
Fig. 8 is a result of scheduling power balance optimization, and fig. 10 is a result of scheduling hydrogen power balance optimization, during electricity price valley period and peaceful period, wind power preferentially meets the hydrogen load demand by electrical hydrogen production, and stores a part of hydrogen energy in a hydrogen storage tank, another part of wind power supplies the electrical load, and the insufficient part is supplied by low-price electrical energy; and in the electricity price peak period, the electricity load consumption wind power proportion is increased, so that the electricity purchasing quantity is reduced, the electricity hydrogen production consumption wind power proportion is reduced, and the insufficient part releases hydrogen energy from the hydrogen storage tank.
Table 3 is a comparison of the costs of the scheduling schemes. As can be seen from table 3, by comparing the operation cost, the wind curtailment cost, the hydrogen purchase cost, and the comprehensive target cost of each scheduling scheme:
TABLE 3 cost comparison of scheduling schemes
Scene Running cost/$ Wind curtailment penalty cost/$ Cost of hydrogen purchase/$ Integrated target cost/$
1 50421.8 3198.5 36759.2 90379.5
2 49791.1 2766.4 36759.2 89316.7
3 50201.8 3198.4 36759.2 90159.4
4 49571.3 2766.3 36759.2 89096.8
5 50666.7 3.8e-05 0 50666.7
6 49808.6 0.62 0 49809.2
1) Compared with the scenario 1, the scenario 2 considers the electricity load demand response, and reduces 1062.8 yuan of the total system cost for peak clipping and valley filling of the electricity load demand curve; 2) compared with the scene 1, the scene 3 considers the heat load demand response, and reduces the total system cost by 220.1 yuan for peak clipping and valley filling of a heat load demand curve; 3) compared with the scenario 1, the scenario 4 considers the comprehensive demand response of energy-side electricity and heat, the total cost is reduced by 1282.7 yuan, and simultaneously, the cost is lower than that when the single type of load demand response is considered, and the total cost is reduced by 219.9 yuan and 1062.6 yuan respectively compared with the total cost of the scenario 2 and the scenario 3; 4) compared with the scenario 1, the scenario 5 considers the demand response of the energy supply side, and the hydrogen load demand of the energy supply side in the electricity price valley period and the peacetime period is supplied by wind power; and in the peak period of electricity price, the hydrogen load is shared by wind power and outsourcing hydrogen energy. Therefore, the hydrogen purchase cost is greatly reduced, the wind power consumption level is enhanced, the wind abandon punishment cost is reduced, and the total cost is reduced by 39712.79996 yuan; 5) compared with scenario 1, scenario 6 allows for bidirectional demand response with energy supply, and the total cost is reduced by 40570.28 yuan. Meanwhile, the total cost of the scene 6 considering only the energy-use side comprehensive demand response is reduced by 39287.58 yuan compared with the scene 4, and the total cost of the demand response considering only the energy-supply side is reduced by 857.5 yuan compared with the scene 5. In conclusion, the energy utilization side comprehensive demand response is better in economical efficiency than the single type load demand response, and the energy supply bidirectional demand response can obtain better system economical efficiency than the energy utilization side or energy supply side unidirectional demand response.
In summary, the method provided by the present invention flexibly adjusts the resources of the energy utilization side and the energy supply side, and better realizes the multi-energy complementation on the premise of ensuring the energy utilization requirement, thereby achieving the optimal scheduling of economy. Because the energy utilization side needs to respond to the peak clipping and valley filling effect and the surplus wind power at the energy supply side is used for producing hydrogen and converting the hydrogen energy into the wind power, the wind power consumption rate of the system can be improved. Therefore, the wind power grid-connection space of the system is further increased. The optimal scheduling method of the multi-energy system considering supply and demand bidirectional demand response provided by the invention has effectiveness and rationality
Example 2
The embodiment provides a multi-energy system optimization scheduling device considering supply and demand bidirectional demand response, including:
the parameter acquisition module is used for determining and acquiring basic operation parameters of the comprehensive energy system;
the comprehensive energy system model building module is used for building a comprehensive energy system model considering electricity-heat multi-energy flow;
the energy utilization side comprehensive demand response model building module is used for building an energy utilization side comprehensive demand response model considering electric load and heat load;
the energy supply side demand response model building module is used for building an energy supply side demand response model considering wind power, electric hydrogen production and a hydrogen storage tank;
the objective function construction module is used for constructing an objective function with the lowest energy utilization satisfaction degree and operation cost of the comprehensive energy system;
the constraint condition acquisition module is used for acquiring constraint conditions of energy balance constraint, system network constraint and equipment model constraint;
and the objective function solving module is used for solving the objective function to obtain the day-ahead optimized scheduling scheme of the comprehensive energy system.
A computing device, comprising:
one or more processing units;
a storage unit for storing one or more programs,
wherein the one or more programs, when executed by the one or more processing units, cause the one or more processing units to perform the multi-energy system optimization scheduling method taking into account supply and demand bi-directional demand responses as described above; it is noted that the computing device may include, but is not limited to, a processing unit, a storage unit; those skilled in the art will appreciate that the computing device including the processing unit, the memory unit do not constitute a limitation of the computing device, may include more components, or combine certain components, or different components, for example, the computing device may also include input output devices, network access devices, buses, etc.
A computer readable storage medium having non-volatile program code executable by a processor, the computer program when executed by the processor implementing the steps of the multi energy system optimization scheduling method taking into account supply and demand bi-directional demand responses as described above; it should be noted that the readable storage medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof; the program embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. For example, program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the C programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, or entirely on a remote computing device or server. In situations involving remote computing devices, the remote computing devices may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to external computing devices (e.g., through the internet using an internet service provider).
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 (10)

1. The optimal scheduling method of the multi-energy system considering supply and demand bidirectional demand response is characterized by comprising the following steps:
determining and acquiring basic operation parameters of the comprehensive energy system;
constructing a comprehensive energy system model considering the electricity-heat multi-energy flow;
constructing an energy utilization side comprehensive demand response model considering electric load and thermal load;
constructing an energy supply side demand response model considering wind power, electric hydrogen production and a hydrogen storage tank;
constructing a target function with the lowest energy consumption satisfaction degree and operation cost of the comprehensive energy system;
acquiring constraint conditions of energy balance constraint, system network constraint and equipment model constraint;
and solving the objective function to obtain a day-ahead optimized scheduling scheme of the comprehensive energy system.
2. The optimal scheduling method of multi-energy system considering supply and demand bi-directional demand response according to claim 1, wherein constructing an integrated energy system model considering electricity-heat multi-energy flow comprises the following steps:
carrying out power grid modeling on the radial power distribution network: the method comprises the following steps of establishing a network model of the power distribution network by adopting a Dist-Flow power Flow equation, wherein the network model comprises an active power Flow equation, a reactive power Flow equation and a voltage equation, namely:
Figure FDA0003460126920000011
Figure FDA0003460126920000012
Figure FDA0003460126920000013
wherein, Pi,tAnd Qi,tRespectively the active power and the reactive power on the line from the power grid node i to the node i +1 at the moment t; r isiThe resistance of the line from the node i to the node i +1 at the time t;
Figure FDA0003460126920000014
and
Figure FDA0003460126920000015
respectively an active load at a power grid node i at the moment t and active power of a power supply; x is the number ofiThe reactance of the line from node i to node i +1 at time t;
Figure FDA0003460126920000016
and
Figure FDA0003460126920000017
respectively the reactive load and the power supply reactive power at a grid node i at the time t; vi,tIs the voltage at node i at time t;
introducing variables shown in the formula (4), and performing second-order cone relaxation on the distribution network model to obtain a formula (5):
Figure FDA0003460126920000018
Figure FDA0003460126920000019
and (3) modeling a heat supply network: the heat variation relationship of the pipe ij can be expressed as:
Figure FDA0003460126920000021
in the formula, Hi,tAnd Hj,tRespectively the heat energy flow at the node i and the node j at the time t in the heat supply network system; l isijIs the length of conduit ij; sigma RunitThe thermal resistance of the unit length from the heating medium to the environment medium; t isij，tThe temperature of a heating medium of a pipeline ij at the moment t in the heat supply network system; t isa,tIs the ambient temperature around the pipeline; c is the specific heat capacity of water; m isi,tAnd mj,tRespectively are heat medium mass flows at the node i and the node j; t isi,tAnd Tj,tRespectively the temperature of the heating medium at the node i and the node j;
the pipe end temperature, due to the pipe heat transfer losses due to the temperature difference between the pipe and the surrounding environment, can be expressed as:
Figure FDA0003460126920000022
Figure FDA0003460126920000023
wherein alpha isijIs the loss constant of the pipe ij; k is a radical ofijIs the heat loss coefficient of the pipe ij; m isij，tIs the heat medium mass flow of the pipe ij at the time t in the heat supply network system.
3. The optimal scheduling method for multi-energy system considering supply and demand bidirectional demand response as claimed in claim 2, wherein the step of constructing the energy utilization side comprehensive demand response model considering the electric load and the thermal load comprises the following steps:
modeling the electric load demand response:
Figure FDA0003460126920000024
Figure FDA0003460126920000025
Figure FDA0003460126920000026
wherein the content of the first and second substances,
Figure FDA0003460126920000027
and
Figure FDA0003460126920000028
respectively representing the fixed electric load, the transferable electric load and the total electric load at the t moment at the power grid node i;
Figure FDA0003460126920000029
representing the upper limit of the transferable electrical load at grid node i;
Figure FDA00034601269200000210
representing the total transferable electric load in a dispatching cycle at a power grid node i;
modeling of thermal load demand response:
Figure FDA00034601269200000211
Figure FDA00034601269200000212
Figure FDA00034601269200000213
wherein the content of the first and second substances,
Figure FDA00034601269200000214
and
Figure FDA00034601269200000215
respectively representing the fixed heat load, the transferable heat load and the total heat load at the heat supply network node k at the moment t;
Figure FDA0003460126920000031
represents an upper bound for the transferable thermal load at heat grid node k;
Figure FDA0003460126920000032
representing the total amount of transferable thermal load during the scheduling period at heat network node k.
4. The optimal scheduling method of the multi-energy system considering supply and demand bidirectional demand response as claimed in claim 3, wherein constructing an energy supply side demand response model considering wind power, electric hydrogen production and hydrogen storage tanks comprises the following steps:
constructing an electric hydrogen production and storage tank model:
Figure FDA0003460126920000033
Figure FDA0003460126920000034
Figure FDA0003460126920000035
Figure FDA0003460126920000036
Figure FDA0003460126920000037
Figure FDA0003460126920000038
Ich,t,i+Idis,t,i≤1 (21)
wherein the content of the first and second substances,
Figure FDA0003460126920000039
and H2t,iRespectively the power consumption power and the hydrogen production power of the electrolytic cell at the power grid node i at the time t; etaiRepresenting the hydrogen production efficiency of the electrolytic cell at the power grid node i;
Figure FDA00034601269200000310
representing the maximum power consumption of the electrolytic cell at the node i of the power grid;
Figure FDA00034601269200000311
and
Figure FDA00034601269200000312
maximum power of hydrogen charging and discharging of a hydrogen storage tank at a power grid node i is respectively set;
Figure FDA00034601269200000313
and
Figure FDA00034601269200000314
the power of charging and discharging hydrogen of the hydrogen storage tank at the power grid node i at the moment t is respectively;
Figure FDA00034601269200000315
and
Figure FDA00034601269200000316
the hydrogen storage amounts of the hydrogen storage tank at t-1 and t moments at the power grid node i are respectively;
Figure FDA00034601269200000317
respectively representing the minimum value and the maximum value of the hydrogen storage capacity of the hydrogen storage tank at the power grid node i; alpha is alphap2h,iThe loss rate of the hydrogen storage tank at the power grid node i is obtained; etach,iAnd ηdis,iRespectively representing the hydrogen energy storage efficiency and the hydrogen energy release efficiency of a hydrogen storage tank at a power grid node i; i isch,t,iAnd Idis,t,iThe energy storage state is a variable of 0-1 when the hydrogen storage tank at the power grid node i is in an energy storage or release state at the moment t;
and (3) wind power scheduling constraint condition formulation:
Figure FDA00034601269200000318
Figure FDA00034601269200000319
Figure FDA00034601269200000320
wherein the content of the first and second substances,
Figure FDA00034601269200000321
representing the predicted wind power at the t moment of a power grid node i;
Figure FDA00034601269200000322
representing the actual wind power at time t at grid node i,
Figure FDA00034601269200000323
and
Figure FDA00034601269200000324
and respectively representing the wind power parts participating in hydrogen production by electricity and system scheduling at the time t at the power grid node i.
5. The optimal scheduling method for multi-energy system considering supply and demand bidirectional demand response as claimed in claim 4, wherein the step of constructing the objective function with lowest energy utilization satisfaction and operation cost of the integrated energy system comprises the following steps:
and (3) making an operation cost objective function:
for the type of unit involved, the operating costs include the cost of powering the equipment and the cost of purchasing and selling electricity:
Figure FDA0003460126920000041
wherein, CenergyThe cost of energy supply to the system is reduced,
Figure FDA0003460126920000042
for a time t period of n equipment operating costs of the unit, wherein m3The system comprises a gas turbine, a gas boiler, a wind power and cogeneration unit, an electric energy storage and heat storage tank and a hydrogen storage tank, wherein the gas turbine, the gas boiler, the wind power and cogeneration unit, the electric energy storage and heat storage tank and the hydrogen storage tank are respectively arranged on a gas turbine, a GB, a CHP, a WT and an HES;
Figure FDA0003460126920000043
the total number of the energy supply units;
Figure FDA0003460126920000044
the electricity purchasing and selling cost is t time period;
Figure FDA0003460126920000045
is the hydrogen purchase cost for the time period t;
Figure FDA0003460126920000046
wherein the content of the first and second substances,
Figure FDA0003460126920000047
the t time period and the electricity purchasing power and the electricity selling power of the superior power grid are respectively,
Figure FDA0003460126920000048
respectively obtaining the electricity purchasing price and the electricity selling price at the time t;
Figure FDA0003460126920000049
wherein p ist、λtHydrogen purchasing power and hydrogen purchasing price in a time period t are respectively;
and (3) setting a wind abandon punishment cost:
Figure FDA00034601269200000410
wherein, CpunisRepresenting wind curtailment penalty cost; c. CwtRepresenting a wind curtailment penalty coefficient;
the user uses an energy satisfaction target function to make:
Figure FDA00034601269200000411
wherein, CdrRepresenting wind curtailment penalty cost; v. ofe、αeTo use the electrical preference coefficient, vh、αhIs the thermal preference coefficient;
and (3) making a comprehensive optimization objective function:
minF＝min(Cenergy+Cpunis+Cdr) (30)
wherein F is the comprehensive cost of the system.
6. The optimal scheduling method for the multi-energy system considering supply and demand bidirectional demand response as claimed in claim 5, wherein the equipment model constraints include unit output upper and lower limit constraints, unit climbing constraints, energy storage equipment constraints, and extraction condensing thermoelectric unit energy conversion constraints.
7. The optimal scheduling method for multi-energy system considering supply and demand bidirectional demand response as claimed in claim 6, wherein the comprehensive energy system model is solved by using a commercial solver CPLEX.
8. The multi-energy system optimization scheduling device considering supply and demand bidirectional demand response is characterized in that: the method comprises the following steps:
the parameter acquisition module is used for determining and acquiring basic operation parameters of the comprehensive energy system;
the comprehensive energy system model building module is used for building a comprehensive energy system model considering electricity-heat multi-energy flow;
the energy utilization side comprehensive demand response model building module is used for building an energy utilization side comprehensive demand response model considering electric load and heat load;
the energy supply side demand response model building module is used for building an energy supply side demand response model considering wind power, electric hydrogen production and a hydrogen storage tank;
the objective function construction module is used for constructing an objective function with the lowest energy utilization satisfaction degree and operation cost of the comprehensive energy system;
the constraint condition acquisition module is used for acquiring constraint conditions of energy balance constraint, system network constraint and equipment model constraint;
and the objective function solving module is used for solving the objective function to obtain the day-ahead optimized scheduling scheme of the comprehensive energy system.
9. A computing device, characterized by: the method comprises the following steps: one or more processing units; a storage unit to store one or more programs that, when executed by the one or more processing units, cause the one or more processing units to perform the method of any of claims 1-7.
10. A computer-readable storage medium with non-volatile program code executable by a processor, characterized in that the computer program realizes the steps of the method according to any one of claims 1 to 7 when executed by the processor.
CN202210015049.9A 2022-01-07 2022-01-07 Multi-energy system optimal scheduling method and device considering supply and demand bidirectional demand response Pending CN114358431A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210015049.9A CN114358431A (en) 2022-01-07 2022-01-07 Multi-energy system optimal scheduling method and device considering supply and demand bidirectional demand response

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210015049.9A CN114358431A (en) 2022-01-07 2022-01-07 Multi-energy system optimal scheduling method and device considering supply and demand bidirectional demand response

Publications (1)

Publication Number Publication Date
CN114358431A true CN114358431A (en) 2022-04-15

Family

ID=81106905

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210015049.9A Pending CN114358431A (en) 2022-01-07 2022-01-07 Multi-energy system optimal scheduling method and device considering supply and demand bidirectional demand response

Country Status (1)

Country Link
CN (1) CN114358431A (en)

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114861546A (en) * 2022-05-20 2022-08-05 华北电力大学 Electric heating hydrogen multi-energy complementary scheduling method and system based on hydrogen-fired gas turbine
CN115062982A (en) * 2022-06-20 2022-09-16 西南石油大学 Comprehensive energy system optimal scheduling method based on energy supply of composite energy pipeline
CN115619006A (en) * 2022-09-23 2023-01-17 河海大学 Electricity-gas-hydrogen series-parallel connection comprehensive energy system optimization scheduling method considering auxiliary service
CN115860219A (en) * 2022-11-30 2023-03-28 国网北京市电力公司 Hydrogen-containing energy storage comprehensive energy system optimization operation method, device, equipment and medium
CN115907350A (en) * 2022-11-04 2023-04-04 华北电力大学 Energy management method and system of building comprehensive energy system
CN116128163A (en) * 2023-04-13 2023-05-16 国网天津静海供电有限公司 Comprehensive energy optimization method and device considering green hydrogen production and storage and user satisfaction
CN116454987A (en) * 2023-03-17 2023-07-18 长电新能有限责任公司 Energy storage optimization method and system for joint scheduling with new energy
CN114861546B (en) * 2022-05-20 2024-05-24 华北电力大学 Electric heating hydrogen multi-energy complementary scheduling method and system based on hydrogen-burning gas turbine

Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114861546A (en) * 2022-05-20 2022-08-05 华北电力大学 Electric heating hydrogen multi-energy complementary scheduling method and system based on hydrogen-fired gas turbine
CN114861546B (en) * 2022-05-20 2024-05-24 华北电力大学 Electric heating hydrogen multi-energy complementary scheduling method and system based on hydrogen-burning gas turbine
CN115062982A (en) * 2022-06-20 2022-09-16 西南石油大学 Comprehensive energy system optimal scheduling method based on energy supply of composite energy pipeline
CN115062982B (en) * 2022-06-20 2024-05-31 西南石油大学 Comprehensive energy system optimal scheduling method based on energy supply of composite energy pipeline
CN115619006B (en) * 2022-09-23 2023-12-05 河海大学 Electric-gas-hydrogen series-parallel comprehensive energy system optimization scheduling method considering auxiliary service
CN115619006A (en) * 2022-09-23 2023-01-17 河海大学 Electricity-gas-hydrogen series-parallel connection comprehensive energy system optimization scheduling method considering auxiliary service
CN115907350A (en) * 2022-11-04 2023-04-04 华北电力大学 Energy management method and system of building comprehensive energy system
CN115907350B (en) * 2022-11-04 2023-08-08 华北电力大学 Energy management method and system for building integrated energy system
CN115860219A (en) * 2022-11-30 2023-03-28 国网北京市电力公司 Hydrogen-containing energy storage comprehensive energy system optimization operation method, device, equipment and medium
CN115860219B (en) * 2022-11-30 2023-08-29 国网北京市电力公司 Optimized operation method, device, equipment and medium for hydrogen-containing energy storage comprehensive energy system
CN116454987A (en) * 2023-03-17 2023-07-18 长电新能有限责任公司 Energy storage optimization method and system for joint scheduling with new energy
CN116454987B (en) * 2023-03-17 2023-10-20 长电新能有限责任公司 Energy storage optimization method and system for joint scheduling with new energy
CN116128163A (en) * 2023-04-13 2023-05-16 国网天津静海供电有限公司 Comprehensive energy optimization method and device considering green hydrogen production and storage and user satisfaction

Similar Documents

Publication Publication Date Title
CN114358431A (en) Multi-energy system optimal scheduling method and device considering supply and demand bidirectional demand response
CN108154309B (en) Energy internet economic dispatching method considering multi-load dynamic response of cold, heat and electricity
CN106372742A (en) Power-to-gas multi-source energy storage type microgrid day-ahead optimal economic dispatching method
CN111950807B (en) Comprehensive energy system optimization operation method considering uncertainty and demand response
CN112068436B (en) Layered and distributed control method and system for comprehensive energy system of industrial park
CN112701687B (en) Robust optimization operation method of gas-electricity distribution network system considering price type combined demand response
Ju et al. A Tri-dimensional Equilibrium-based stochastic optimal dispatching model for a novel virtual power plant incorporating carbon Capture, Power-to-Gas and electric vehicle aggregator
CN113095791A (en) Operation method and system of comprehensive energy system
CN114358432A (en) Multi-energy system optimization scheduling method and device considering demand response and carbon transaction
CN116911533B (en) Multi-microgrid energy sharing method for regional comprehensive energy system
Zhang et al. Bi-level optimization dispatch of integrated-energy systems with P2G and carbon capture
Wu et al. Dynamic pricing and energy management of hydrogen-based integrated energy service provider considering integrated demand response with a bi-level approach
Cai et al. Optimal scheduling of a hybrid AC/DC multi-energy microgrid considering uncertainties and Stackelberg game-based integrated demand response
CN114881328A (en) Comprehensive energy system economic dispatching method considering gas network hydrogen mixing and low-carbon reward
CN113298407A (en) Industrial park electricity-gas comprehensive energy system optimization scheduling model establishing method
CN112182915A (en) Optimized scheduling method and system for cooperatively promoting wind power consumption
CN109376406B (en) Energy supply system superstructure model, modeling method, computer device and storage medium
CN109193666B (en) General energy bus-based comprehensive energy system time sequence energy flow calculation method
CN111724026A (en) Optimization method for coupling operation of multi-energy network and water distribution network
CN115693797A (en) Power distribution network scheduling method, medium and system considering V2G and demand response
CN113128746A (en) Multi-agent-based multi-main-body combined optimization operation method for comprehensive energy system
Liang et al. Research on optimization scheduling of integrated electricity-gas system considering carbon trading and P2G operation characteristics
CN115659585A (en) Micro-energy network low-carbon cooperative scheduling method and device considering demand response, memory and equipment
CN116187648A (en) Virtual power plant thermoelectric combination optimization scheduling method based on thermal decoupling
CN115663832A (en) Source network and storage interactive game operation strategy for multi-energy complementary power distribution system

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination