CN112686425A - Energy internet optimal scheduling method and system based on cooperative game - Google Patents

Energy internet optimal scheduling method and system based on cooperative game Download PDF

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CN112686425A
CN112686425A CN202011428632.XA CN202011428632A CN112686425A CN 112686425 A CN112686425 A CN 112686425A CN 202011428632 A CN202011428632 A CN 202011428632A CN 112686425 A CN112686425 A CN 112686425A
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energy internet
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CN112686425B (en
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钟永洁
常晓勇
李玉平
王玉婷
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Nanjing SAC Automation Co Ltd
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Nanjing SAC Automation Co Ltd
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Abstract

The invention discloses an energy internet optimal scheduling method and system based on cooperative game, which comprehensively consider the interactive layering characteristics of the energy internet, adopt cooperative game to obtain the energy internet optimal scheduling result, better conform to the actual field of engineering application, facilitate the reduction of the optimal scheduling economic cost and the improvement of the energy efficiency level, consider the pursuit of different energy internet different layer powers, and facilitate the balance of interest conflicts and contradictions of all parties; meanwhile, the invention considers the multiple requirements of energy Internet optimization scheduling from the aspects of economy and energy efficiency, and better meets the benefit requirements of different optimization scheduling modes.

Description

Energy internet optimal scheduling method and system based on cooperative game
Technical Field
The invention relates to an energy internet optimal scheduling method and system based on cooperative game, and belongs to the field of energy internet.
Background
The energy Internet is characterized by electricity as a center, a network as a platform, intelligent interconnection, clean substitution and electric energy substitution. Compared with the traditional power grid, the energy Internet is a configuration platform for promoting large-scale development and utilization of clean energy, and is an innovative platform for supporting continuous emergence of new technology, new state and new mode. The energy internet integrates a new energy technology, an intelligent technology, an information technology and a network technology which are the most critical of a new technological revolution, and the open interactive and active self-healing intelligent power distribution and utilization is an important link of energy internet construction. The construction of the energy Internet is accelerated, the necessary way for promoting the clean transformation of energy is provided, the construction of the energy Internet promotes the continuous evolution and development of the structural form, the energy form, the control form, the equipment form, the communication mode and the like of a power distribution network, the functions and the forms of the power distribution network are obviously changed, and higher requirements are provided for the power supply safety, the reliability, the economy and the cleanness of the region.
At present, most of the existing energy internet optimization scheduling methods and technologies are modeling and optimization scheduling aiming at a certain specific level of energy internet architecture, are mostly concentrated under a certain single optimization operation strategy, are not in accordance with engineering application practice, lack of relatively comprehensive comparison verification analysis, and cannot balance benefit conflicts and contradictions among all parties.
Disclosure of Invention
The invention provides an energy internet optimal scheduling method and system based on cooperative game, and solves the problems disclosed in the background technology.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
an energy internet optimization scheduling method based on cooperative game comprises the following steps,
acquiring the information of the lower-layer regional energy Internet, and constructing a lower-layer regional energy Internet energy efficiency optimization model;
collecting upper-layer energy internet information and constructing an upper-layer energy internet economic optimization model;
and (4) considering the interactive layering characteristics of the lower-layer regional energy Internet and the upper-layer regional energy Internet, and performing cooperative game according to the lower-layer regional energy Internet energy efficiency optimization model and the upper-layer regional energy Internet economic optimization model to obtain an energy Internet optimization scheduling result.
Acquiring the information of the lower-layer regional energy Internet, constructing an energy conversion equipment model of the lower-layer regional energy Internet, and constructing a lower-layer regional energy Internet energy efficiency optimization model based on the energy conversion equipment model; the method comprises the steps of collecting upper-layer energy internet information, building an energy conversion equipment model of the upper-layer energy internet, and building an upper-layer energy internet economic optimization model based on the energy conversion equipment model.
The energy efficiency optimization model of the lower-layer regional energy Internet is as follows,
an objective function:
Figure BDA0002825723190000021
wherein the content of the first and second substances,
Figure BDA0002825723190000022
is composed of
Figure BDA0002825723190000029
Efficiency;
Figure BDA0002825723190000023
for cooling load of output
Figure BDA00028257231900000210
The value, NT, is the optimized scheduling period, Δ t is the optimized scheduling simulation step length,
Figure BDA0002825723190000024
respectively the temperature of the cooling working environment and the temperature of a reference point,
Figure BDA0002825723190000025
is the cooling load at time t;
Figure BDA0002825723190000026
for the outgoing heating load
Figure BDA00028257231900000211
The value of the one or more of,
Figure BDA0002825723190000027
respectively as the temperature of the heating working environment and the temperature of a reference point,
Figure BDA0002825723190000028
is the thermal load at time t;
Figure BDA0002825723190000031
load of power supply for output
Figure BDA00028257231900000314
The value of the one or more of,
Figure BDA0002825723190000032
is the electrical load at time t;
Figure BDA0002825723190000033
for the supply of natural gas to the output
Figure BDA00028257231900000315
The value of the one or more of,
Figure BDA0002825723190000034
is the natural gas load at time t;
Figure BDA0002825723190000035
external network for input
Figure BDA00028257231900000316
The value of the one or more of,
Figure BDA0002825723190000036
respectively different primary energy source permeability eta of renewable energy, coal-fired thermal power generating unit and gas unit at t moment in external electricity purchasecoal、ηgasRespectively the working efficiency of a coal-fired thermal power generating unit and a gas generating unit，
Figure BDA0002825723190000037
The purchased electric power at the time t;
Figure BDA0002825723190000038
for input of renewable energy
Figure BDA00028257231900000317
The value of the one or more of,
Figure BDA0002825723190000039
wind power generation power and photovoltaic power generation power at the moment t respectively;
Figure BDA00028257231900000310
for input of biomass
Figure BDA00028257231900000318
The value of the one or more of,
Figure BDA00028257231900000311
is the fuel consumption of the biomass boiler at the time t, ζbbBeing biomass
Figure BDA00028257231900000319
A factor;
Figure BDA00028257231900000312
for the input of natural gas
Figure BDA00028257231900000320
The value of the one or more of,
Figure BDA00028257231900000313
for the natural gas supply at time t, ζgOf natural gas
Figure BDA00028257231900000321
Factor(s)
Constraint conditions are as follows:
the output and climbing capacity of the energy conversion equipment, the operation constraint of the starting times in the optimized dispatching cycle,
Figure BDA0002825723190000041
wherein GT, GB, BB, GSHP, ECHI and ACHI represent energy conversion equipment units which are respectively a gas turbine, a gas boiler, a biomass boiler, a ground source heat pump, an electric refrigerator and an absorption refrigerator;
Figure BDA0002825723190000042
the method comprises the steps of obtaining a Boolean variable of the running state of the unit of the energy conversion equipment at the moment t;
Figure BDA0002825723190000043
the Boolean variable is the running state Boolean variable of the energy conversion equipment unit at the time t + 1; deltaunitThe cutter coefficient of the unit of the energy conversion equipment;
Figure BDA0002825723190000044
respectively the rated electric loading machine capacity, the hot loading machine capacity and the cold loading machine capacity of the unit of the energy conversion equipment;
Figure BDA0002825723190000045
respectively the electric power, the thermal power and the cold power of the energy conversion equipment unit at the time t;
Figure BDA0002825723190000046
respectively the electric power, the thermal power and the cold power of the energy conversion equipment unit at the moment t + 1;
Figure BDA0002825723190000047
the ramp rates under the electric power, the thermal power and the cold power of the energy conversion equipment unit are respectively;
Figure BDA0002825723190000048
the climbing rates of electric power, thermal power and cold power of the energy conversion equipment unit are respectively;
Figure BDA0002825723190000049
setting the upper limit of the starting times of the unit of the energy conversion equipment in the optimized scheduling period;
the minimum stop-and-start time constraints of the energy conversion equipment,
Figure BDA00028257231900000410
wherein the content of the first and second substances,
Figure BDA00028257231900000411
respectively the duration time of a starting state and the duration time of a stopping state of the energy conversion equipment unit at the time t-1;
Figure BDA00028257231900000412
respectively setting the minimum continuous starting time and the minimum continuous shutdown time of the unit of the energy conversion equipment;
Figure BDA00028257231900000413
the Boolean variable is the running state Boolean variable of the energy conversion equipment unit at the time t-1;
ground source heat pumps allow for operating time period operating constraints,
Figure BDA0002825723190000051
wherein, Tstart、TendRespectively setting the starting time and the ending time of the working time period of the ground source heat pump;
Figure BDA0002825723190000058
the Boolean variable is the running state Boolean variable of the ground source heat pump at the moment t; kappaGSHPThe maximum working time of the ground source heat pump in the optimized dispatching cycle accounts for the ratio;
the waste heat recovery capability of the waste heat recovery device is restricted,
Figure BDA0002825723190000052
wherein the content of the first and second substances,
Figure BDA0002825723190000053
respectively recovering the residual heat quantity and the maximum residual heat recovery power of the residual heat recovery device at the moment t;
the output of the new energy is restricted,
Figure BDA0002825723190000054
wherein the content of the first and second substances,
Figure BDA0002825723190000055
the upper limit of the output of the wind power at the moment t is defined;
Figure BDA0002825723190000056
the upper limit of the output of the photovoltaic at the moment t is defined; sigmawt、σpvMaximum wind abandon rate and maximum light abandon rate are respectively allowed in the optimized scheduling period;
the energy balance is restrained by the constraint of energy balance,
Figure BDA0002825723190000057
wherein the content of the first and second substances,
Figure BDA0002825723190000061
electric power of the gas turbine at the time t;
Figure BDA0002825723190000062
the power consumption of the electric refrigerator, the ice storage air conditioner and the ground source heat pump at the time t is respectively;
Figure BDA0002825723190000063
respectively a ground source heat pump, a biomass boiler and a gas boilerThermal power of the furnace at time t;
Figure BDA0002825723190000064
the recovered heat power of the ice storage air conditioner at the time t is obtained;
Figure BDA0002825723190000065
the cold powers of the electric refrigerator, the ice storage air conditioner, the ground source heat pump refrigeration and the absorption refrigerator at the moment t are respectively;
Figure BDA0002825723190000066
fuel input quantities of the gas turbine and the gas boiler at the time t are respectively;
Figure BDA0002825723190000067
is the natural gas supply at time t;
the tie-line power transfer limit constraints,
Figure BDA0002825723190000068
wherein the content of the first and second substances,P link、
Figure BDA0002825723190000069
respectively representing the lower limit of purchased electric power and the upper limit of purchased electric power at the time t;F link、
Figure BDA00028257231900000610
the lower limit of the natural gas power and the upper limit of the natural gas power are respectively at the moment t.
The economic optimization model of the upper-layer energy Internet is that,
an objective function:
Figure BDA00028257231900000611
wherein the content of the first and second substances,
Figure BDA00028257231900000612
fre、fGW、fCHP、fTUrespectively calculating the running economic cost of the upper-layer energy internet, a new energy punishment economic cost function, an air source output economic cost function, a combined heat and power unit running economic cost function and a thermal power unit economic cost function, wherein NT is an optimized scheduling period, and delta t is an optimized scheduling simulation step length;
Figure BDA00028257231900000613
Figure BDA00028257231900000614
a polynomial coefficient of an economic cost function for the operation of the cogeneration unit,
Figure BDA00028257231900000615
respectively supplying the output electric power and the output thermal power of the cogeneration at the moment t;
Figure BDA0002825723190000071
πwt、πpvrespectively a wind curtailment penalty factor and a light curtailment penalty factor,
Figure BDA0002825723190000072
is the upper limit of the output of the wind power at the moment t,
Figure BDA0002825723190000073
the upper limit of the output of the photovoltaic at the moment t,
Figure BDA0002825723190000074
wind power generation power and photovoltaic power generation power at the moment t respectively;
Figure BDA0002825723190000075
Figure BDA0002825723190000076
is a polynomial coefficient of an economic cost function of the thermal power generating unit,
Figure BDA0002825723190000077
generating power of the thermal power generating unit at the moment t;
Figure BDA0002825723190000078
λGWis a natural gas price factor and is a natural gas price factor,
Figure BDA0002825723190000079
the output of the air source at the time t;
constraint conditions are as follows:
the rotational back-up constraint of the power system,
Figure BDA00028257231900000710
wherein the content of the first and second substances,
Figure BDA00028257231900000711
the standby capacity of the coal-fired thermal power generating unit in the upper-layer energy internet is respectively the upward rotation standby capacity and the downward rotation standby capacity at the moment t; SRup、SRdownThe method comprises the steps that the upward rotation standby capacity and the downward rotation standby capacity of a power system in the upper-layer energy internet are respectively set;
Figure BDA00028257231900000712
the ramp-up speed and the ramp-down speed of the thermal power generating unit are respectively set;
Figure BDA00028257231900000713
the installed capacity of the thermal power generating unit at the moment t is obtained; deltaTUThe coefficient of the thermal power generating unit is the coefficient of the thermal power generating unit; omegaTUThe method comprises the steps of (1) collecting thermal power generating units;
the power generation permeability of different types of energy is restricted,
Figure BDA0002825723190000081
wherein the content of the first and second substances,
Figure BDA0002825723190000082
respectively the permeability of different primary energy sources of renewable energy sources, coal-fired thermal power generating units and gas generating units in external electricity purchase at the time t,
Figure BDA0002825723190000083
the total amount of renewable energy power generation, the total amount of power generation of a coal-fired unit, the total amount of power generation of a gas unit and the total amount of power generation of all types of generator sets of the upper-layer energy internet power system at the time t are respectively.
And considering the interactive layering characteristics of the lower-layer regional energy Internet and the upper-layer regional energy Internet, and sequentially performing an outer-layer cooperation game and an inner-layer cooperation game according to the lower-layer regional energy Internet energy efficiency optimization model and the upper-layer regional energy Internet economic optimization model to obtain an energy Internet optimization scheduling result.
The outer layer cooperation game is to carry out game by carrying out energy efficiency optimization on the lower layer area energy Internet and economic optimization on the upper layer energy Internet;
the inner layer cooperation game is that the upper layer energy internet carries out the optimization game of the economic cost of the electric power system and the optimization game of the economic cost of the gas system on the basis of the result of the outer layer cooperation game.
And outputting an energy internet optimized scheduling result to the lower-layer energy internet and the upper-layer energy internet.
An energy internet optimization scheduling system based on cooperative game comprises,
an energy efficiency optimization model module: acquiring the information of the lower-layer regional energy Internet, and constructing a lower-layer regional energy Internet energy efficiency optimization model;
an economic optimization model module: collecting upper-layer energy internet information and constructing an upper-layer energy internet economic optimization model;
the cooperative game module: and (4) considering the interactive layering characteristics of the lower-layer regional energy Internet and the upper-layer regional energy Internet, and performing cooperative game according to the lower-layer regional energy Internet energy efficiency optimization model and the upper-layer regional energy Internet economic optimization model to obtain an energy Internet optimization scheduling result.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform a cooperative game-based energy internet optimization scheduling method.
A computing device comprising one or more processors, one or more memories, and one or more programs stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs including instructions for performing a cooperative game-based energy internet optimization scheduling method.
The invention achieves the following beneficial effects: the interactive hierarchical characteristic of the energy Internet is comprehensively considered, the cooperative game is adopted to obtain the energy Internet optimized scheduling result, the engineering application actual field is better met, the optimized scheduling economic cost is reduced, the energy efficiency level is improved, the pursuit of different layer powers of different energy internets is considered, and the benefit conflict and contradiction of each party are balanced; meanwhile, the invention considers the multiple requirements of energy Internet optimization scheduling from the aspects of economy and energy efficiency, and better meets the benefit requirements of different optimization scheduling modes.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
fig. 2 is a structural view of an embodiment.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
As shown in fig. 1, an energy internet optimization scheduling method based on cooperative game includes the following steps:
step 1, collecting information of a lower-layer regional energy internet and an upper-layer regional energy internet; the system specifically comprises an energy flow architecture of a lower-layer regional energy internet, an energy flow architecture of an upper-layer regional energy internet, energy conversion equipment parameters, energy conversion equipment safety operation constraints, energy conversion equipment operation economic cost, physical characteristics of interaction ports of the lower-layer regional energy internet and the upper-layer regional energy internet, typical-day photovoltaic prediction data, typical-day wind power prediction data, typical-day cold load prediction data, typical-day heat load prediction data, typical-day electric load prediction data, typical-day natural gas load prediction data and the like; the lower zone is a small zone, such as a circular zone, and the upper zone is a large zone.
And 2, constructing a lower-layer regional energy Internet energy efficiency optimization model based on the lower-layer regional energy Internet information.
And constructing an energy conversion equipment model of the lower-layer regional energy Internet according to the information of the lower-layer regional energy Internet, and constructing an energy efficiency optimization model of the lower-layer regional energy Internet based on the energy conversion equipment model.
The main energy conversion device models include:
1. a gas turbine model:
Figure BDA0002825723190000101
wherein the content of the first and second substances,
Figure BDA0002825723190000102
electric power, fuel input quantity, thermal power and electric load rate of the gas turbine at the moment t are respectively;
Figure BDA0002825723190000111
respectively representing the power generation efficiency and the energy loss rate of the gas turbine at the moment t;
Figure BDA0002825723190000112
the polynomial coefficient is the power generation efficiency characteristic curve of the gas turbine, and the upper standard n is the polynomial power;
Figure BDA0002825723190000113
the Boolean variable is the operation state Boolean variable of the gas turbine at the time t, the value of the Boolean variable is 1 when the gas turbine operates, and the value of the Boolean variable is 0 when the gas turbine stops.
2. Waste heat recovery equipment model
Figure BDA0002825723190000114
Wherein the content of the first and second substances,
Figure BDA0002825723190000115
respectively representing the output thermal power and the thermal load rate of the waste heat recovery equipment at the moment t;
Figure BDA0002825723190000116
thermal power of the gas turbine at the time t;
Figure BDA0002825723190000117
the working thermal efficiency of the waste heat recovery equipment at the moment t is shown;
Figure BDA0002825723190000118
polynomial coefficients of the characteristic curve of the working thermal efficiency of the waste heat recovery equipment;
Figure BDA0002825723190000119
the Boolean variable is the Boolean variable of the operation state of the waste heat recovery equipment at the time t, the Boolean variable value is 1 when the waste heat recovery equipment operates, and the Boolean variable value is 0 when the waste heat recovery equipment stops.
3. Gas boiler model
Figure BDA00028257231900001110
Wherein the content of the first and second substances,
Figure BDA00028257231900001111
respectively the fuel input quantity, the thermal power and the thermal load rate of the gas-fired boiler at the time t;
Figure BDA00028257231900001112
the working thermal efficiency of the gas boiler at the moment t is shown;
Figure BDA00028257231900001113
polynomial coefficients of the heating efficiency characteristic curve of the gas boiler;
Figure BDA00028257231900001114
the Boolean variable is the operation state Boolean variable of the gas-fired boiler at the time t, the value of the Boolean variable is 1 when the gas-fired boiler equipment operates, and the value of the Boolean variable is 0 when the gas-fired boiler equipment stops.
4. Biomass boiler model
Figure BDA0002825723190000121
Wherein the content of the first and second substances,
Figure BDA0002825723190000122
respectively the fuel input quantity, the thermal power and the thermal load rate of the biomass boiler at the time t;
Figure BDA0002825723190000123
the working thermal efficiency of the biomass boiler at the moment t;
Figure BDA0002825723190000124
polynomial coefficients of a biomass boiler heating efficiency characteristic curve;
Figure BDA0002825723190000125
the Boolean variable value of the biomass boiler at the time t is 1 when the biomass boiler equipment is operated, and the Boolean variable value of the biomass boiler equipment is 0 when the biomass boiler equipment is shut down.
5. Ground source heat pump model
Figure BDA0002825723190000126
Wherein the content of the first and second substances,
Figure BDA0002825723190000127
respectively the heating power and the refrigerating power of the ground source heat pump at the moment t;
Figure BDA0002825723190000128
the consumed power of the ground source heat pump at the moment t is obtained;
Figure BDA0002825723190000129
respectively is a heating working performance factor and a refrigerating working performance factor of the ground source heat pump;
Figure BDA00028257231900001210
the Boolean variable is an operation state Boolean variable at the time t when the ground source heat pump works in a heating state, the Boolean variable takes a value of 1 when the ground source heat pump equipment operates, and the Boolean variable takes a value of 0 when the ground source heat pump equipment stops;
Figure BDA00028257231900001211
the Boolean variable is the Boolean variable of the running state at the time t when the ground source heat pump works in the refrigeration state, the Boolean variable value is 1 when the ground source heat pump equipment runs, and the Boolean variable value is 0 when the ground source heat pump equipment stops.
6. Electric refrigerator model
Figure BDA00028257231900001212
Wherein the content of the first and second substances,
Figure BDA00028257231900001213
respectively the refrigeration power, the power consumption power and the cold load rate of the electric refrigerator at the moment t;
Figure BDA00028257231900001214
at t hours for electric refrigerators respectivelyActual working performance factor and rated working performance factor of the etching;
Figure BDA0002825723190000131
polynomial coefficient of refrigerating performance characteristic curve of the electric refrigerator;
Figure BDA0002825723190000132
the Boolean variable is the Boolean variable of the electric refrigerator in the running state at the time t, the Boolean variable value is 1 when the electric refrigerator is running, and the Boolean variable value is 0 when the electric refrigerator is stopped.
7. Absorption type refrigerator model
Figure BDA0002825723190000133
Wherein the content of the first and second substances,
Figure BDA0002825723190000134
respectively the refrigeration power, the recovered thermal power and the cold load power of the absorption refrigerator at the moment t;
Figure BDA0002825723190000135
the actual working performance factor and the rated working performance factor of the absorption refrigerator at the moment t are respectively;
Figure BDA0002825723190000136
is a polynomial coefficient of a refrigeration performance characteristic curve of the absorption refrigerator; superscript n is a polynomial power;
Figure BDA0002825723190000137
the boolean variable is the boolean variable of the absorption refrigerator in the operating state at time t, the boolean variable takes a value of 1 when the absorption refrigerator is operating, and the boolean variable takes a value of 0 when the absorption refrigerator is shut down.
The lower-layer regional energy Internet energy efficiency optimization model is as follows:
an objective function:
Figure BDA0002825723190000138
wherein the content of the first and second substances,
Figure BDA0002825723190000139
EXout、EXinare respectively as
Figure BDA00028257231900001313
Efficiency, output assembly
Figure BDA00028257231900001314
Value, input sum
Figure BDA00028257231900001315
A value;
Figure BDA00028257231900001310
for cooling load of output
Figure BDA00028257231900001316
The value, NT, is the optimized scheduling period, Δ t is the optimized scheduling simulation step length,
Figure BDA00028257231900001311
respectively the temperature of the cooling working environment and the temperature of a reference point,
Figure BDA00028257231900001312
is the cooling load at time t;
Figure BDA0002825723190000141
for the outgoing heating load
Figure BDA00028257231900001418
The value of the one or more of,
Figure BDA0002825723190000142
respectively as the temperature of the heating working environment and the temperature of a reference point,
Figure BDA0002825723190000143
is the thermal load at time t;
Figure BDA0002825723190000144
load of power supply for output
Figure BDA00028257231900001419
The value of the one or more of,
Figure BDA0002825723190000145
is the electrical load at time t;
Figure BDA0002825723190000146
for the supply of natural gas to the output
Figure BDA00028257231900001420
The value of the one or more of,
Figure BDA0002825723190000147
is the natural gas load at time t;
Figure BDA0002825723190000148
external network for input
Figure BDA00028257231900001421
The value of the one or more of,
Figure BDA0002825723190000149
respectively different primary energy source permeability eta of renewable energy, coal-fired thermal power generating unit and gas unit at t moment in external electricity purchasecoal、ηgasRespectively the working efficiency of a coal-fired thermal power generating unit and the working efficiency of a gas generating unit,
Figure BDA00028257231900001410
the purchased electric power at the time t;
Figure BDA00028257231900001411
for input of renewable energy
Figure BDA00028257231900001422
The value of the one or more of,
Figure BDA00028257231900001412
wind power generation power and photovoltaic power generation power at the moment t respectively;
Figure BDA00028257231900001413
for input of biomass
Figure BDA00028257231900001423
The value of the one or more of,
Figure BDA00028257231900001414
is the fuel consumption of the biomass boiler at the time t, ζbbBeing biomass
Figure BDA00028257231900001424
A factor;
Figure BDA00028257231900001415
for the input of natural gas
Figure BDA00028257231900001425
The value of the one or more of,
Figure BDA00028257231900001416
for the natural gas supply at time t, ζgOf natural gas
Figure BDA00028257231900001426
A factor.
When load demand EXoutWhen determined, the energy efficiency optimization scheduling model is equivalent to the following formula:
Figure BDA00028257231900001417
constraint conditions are as follows:
1. and (3) operation constraint of energy conversion equipment:
the output and climbing capacity of the energy conversion equipment and the operation constraint of the starting times in the optimized scheduling period are as follows:
Figure BDA0002825723190000151
wherein GT, GB, BB, GSHP, ECHI and ACHI represent energy conversion equipment units which are respectively a gas turbine, a gas boiler, a biomass boiler, a ground source heat pump, an electric refrigerator and an absorption refrigerator;
Figure BDA0002825723190000152
the method comprises the steps of obtaining a Boolean variable of the running state of the unit of the energy conversion equipment at the moment t;
Figure BDA0002825723190000153
the running state Boolean variable of the energy conversion equipment unit at the moment of t +1 is set, the Boolean variable value is 1 when the energy conversion equipment unit runs, and the Boolean variable value is 0 when the energy conversion equipment unit is shut down; deltaunitThe cutter coefficient of the unit of the energy conversion equipment;
Figure BDA0002825723190000154
respectively the rated electric loading machine capacity, the hot loading machine capacity and the cold loading machine capacity of the unit of the energy conversion equipment;
Figure BDA0002825723190000155
respectively the electric power, the thermal power and the cold power of the energy conversion equipment unit at the time t;
Figure BDA0002825723190000156
respectively the electric power, the thermal power and the cold power of the energy conversion equipment unit at the moment t + 1;
Figure BDA0002825723190000157
the ramp rates under the electric power, the thermal power and the cold power of the energy conversion equipment unit are respectively;
Figure BDA0002825723190000158
the climbing rates of electric power, thermal power and cold power of the energy conversion equipment unit are respectively;
Figure BDA0002825723190000159
setting the upper limit of the starting times of the unit of the energy conversion equipment in the optimized scheduling period;
minimum shutdown, startup time constraints for energy conversion equipment:
Figure BDA00028257231900001510
wherein the content of the first and second substances,
Figure BDA0002825723190000161
respectively the duration time of a starting state and the duration time of a stopping state of the energy conversion equipment unit at the time t-1;
Figure BDA0002825723190000162
respectively setting the minimum continuous starting time and the minimum continuous shutdown time of the unit of the energy conversion equipment;
Figure BDA0002825723190000163
the Boolean variable is the running state Boolean variable of the energy conversion equipment unit at the time t-1;
ground source heat pumps allow for operating time period operating constraints,
Figure BDA0002825723190000164
wherein, Tstart、TendRespectively setting the starting time and the ending time of the working time period of the ground source heat pump;
Figure BDA0002825723190000165
the Boolean variable is the running state Boolean variable of the ground source heat pump at the moment t, the value of the Boolean variable is 1 when the ground source heat pump equipment runs, and the value of the Boolean variable is 0 when the ground source heat pump equipment stops; kappaGSHPThe maximum working time of the ground source heat pump in the optimized dispatching cycle accounts for the ratio;
the waste heat recovery device and the gas turbine are combined together to operate in a matched mode, the waste heat recovery device mainly recovers and utilizes waste heat smoke of the gas turbine, the start and stop of the waste heat recovery device are synchronous with the gas turbine, and the recovery waste heat capacity of the waste heat recovery device is restricted:
Figure BDA0002825723190000166
wherein the content of the first and second substances,
Figure BDA0002825723190000167
the recovery waste heat quantity and the maximum recovery waste heat power of the waste heat recovery device at the moment t are respectively.
2. And (3) new energy output constraint:
Figure BDA0002825723190000168
wherein the content of the first and second substances,
Figure BDA0002825723190000171
the upper limit of the output of the wind power at the moment t is defined;
Figure BDA0002825723190000172
the upper limit of the output of the photovoltaic at the moment t is defined; sigmawt、σpvThe maximum wind curtailment rate and the maximum light curtailment rate are respectively allowed in the optimized scheduling period.
3. Energy balance constraint:
Figure BDA0002825723190000173
wherein the content of the first and second substances,
Figure BDA0002825723190000174
electric power of the gas turbine at the time t;
Figure BDA0002825723190000175
the power consumption of the electric refrigerator, the ice storage air conditioner and the ground source heat pump at the time t is respectively;
Figure BDA0002825723190000176
respectively the thermal powers of a ground source heat pump, a biomass boiler and a gas boiler at the time t;
Figure BDA0002825723190000177
the recovered heat power of the ice storage air conditioner at the time t is obtained;
Figure BDA0002825723190000178
the cold powers of the electric refrigerator, the ice storage air conditioner, the ground source heat pump refrigeration and the absorption refrigerator at the moment t are respectively;
Figure BDA0002825723190000179
fuel input quantities of the gas turbine and the gas boiler at the time t are respectively;
Figure BDA00028257231900001710
is the natural gas supply at time t.
4. Tie-line power transfer limit constraints:
Figure BDA00028257231900001711
wherein the content of the first and second substances,P link、
Figure BDA00028257231900001712
respectively representing the lower limit of purchased electric power and the upper limit of purchased electric power at the time t;F link、
Figure BDA00028257231900001713
the lower limit of the natural gas power and the upper limit of the natural gas power are respectively at the moment t.
And 3, collecting information of the upper-layer energy Internet, and constructing an economic optimization model of the upper-layer energy Internet.
And constructing an energy conversion equipment model of the upper-layer energy Internet according to the upper-layer energy Internet information, and constructing an economic optimization model of the upper-layer energy Internet based on the energy conversion equipment model.
The main energy conversion device models include:
1. electric boiler model
Figure BDA0002825723190000181
Wherein the content of the first and second substances,
Figure BDA0002825723190000182
respectively representing the power consumption power, the thermal power and the thermal load rate of the electric boiler at the time t;
Figure BDA0002825723190000183
the working thermal efficiency of the electric boiler at the moment t is shown;
Figure BDA0002825723190000184
the rated working thermal efficiency of the electric boiler;
Figure BDA0002825723190000185
polynomial coefficients of the characteristic curve of the heating efficiency of the electric boiler are obtained;
Figure BDA0002825723190000186
the Boolean variable is the operation state Boolean variable of the electric boiler at the time t, the value of the Boolean variable is 1 when the electric boiler equipment operates, and the value of the Boolean variable is 0 when the electric boiler equipment stops.
2. Electric gas conversion model
Figure BDA0002825723190000187
Wherein the content of the first and second substances,
Figure BDA0002825723190000188
respectively the power consumption and the output natural gas power of the electric conversion gas at the time t; etaP2GThe working efficiency of converting electricity into gas;
Figure BDA0002825723190000189
the Boolean variable is the Boolean variable of the running state of the electric power conversion gas at the time t, the Boolean variable value is 1 when the electric power conversion gas equipment runs, and the Boolean variable value is 0 when the electric power conversion gas equipment stops.
3. Combined heat and power model
The global operation point of the cogeneration unit is in the polygon, the operation interval can be described by a series of linear inequality constraints, and the model expression is as follows:
Figure BDA00028257231900001810
wherein the content of the first and second substances,
Figure BDA00028257231900001811
respectively supplying the output electric power and the output thermal power of the cogeneration at the moment t; a isi、bi、ciAnd the ith linear inequality constraint coefficient of the safe operation area is supplied for the cogeneration.
The upper-layer energy internet economic optimization scheduling cost mainly comprises wind abandoning penalty cost, light abandoning penalty cost, gas source natural gas consumption cost, thermoelectric combined supply unit fuel consumption cost and thermal power unit fuel consumption cost, and therefore the upper-layer energy internet economic optimization model is as follows:
an objective function:
Figure BDA0002825723190000191
wherein the content of the first and second substances,
Figure BDA0002825723190000192
fre、fGW、fCHP、fTUrespectively calculating the running economic cost of the upper-layer energy internet, a new energy punishment economic cost function, an air source output economic cost function, a combined heat and power unit running economic cost function and a thermal power unit economic cost function, wherein NT is an optimized scheduling period, and delta t is an optimized scheduling simulation step length;
Figure BDA0002825723190000193
Figure BDA0002825723190000194
a polynomial coefficient of an economic cost function for the operation of the cogeneration unit,
Figure BDA0002825723190000195
respectively supplying the output electric power and the output thermal power of the cogeneration at the moment t;
Figure BDA0002825723190000196
πwt、πpvrespectively a wind curtailment penalty factor and a light curtailment penalty factor,
Figure BDA0002825723190000197
is the upper limit of the output of the wind power at the moment t,
Figure BDA0002825723190000198
the upper limit of the output of the photovoltaic at the moment t,
Figure BDA0002825723190000199
wind power generation power and photovoltaic power generation power at the moment t respectively;
Figure BDA00028257231900001910
Figure BDA00028257231900001911
is a polynomial coefficient of an economic cost function of the thermal power generating unit,
Figure BDA00028257231900001912
generating power of the thermal power generating unit at the moment t;
Figure BDA00028257231900001913
λGWis a natural gas price factor and is a natural gas price factor,
Figure BDA00028257231900001914
the output of the air source at the time t.
Constraint conditions are as follows:
1. the thermal power generating units in the upper-layer energy internet are the main power supply of the power system, the rotation standby constraint of the power system,
Figure BDA0002825723190000201
wherein the content of the first and second substances,
Figure BDA0002825723190000202
the standby capacity of the coal-fired thermal power generating unit in the upper-layer energy internet is respectively the upward rotation standby capacity and the downward rotation standby capacity at the moment t; SRup、SRdownThe method comprises the steps that the upward rotation standby capacity and the downward rotation standby capacity of a power system in the upper-layer energy internet are respectively set;
Figure BDA0002825723190000203
the ramp-up speed and the ramp-down speed of the thermal power generating unit are respectively set;
Figure BDA0002825723190000204
the installed capacity of the thermal power generating unit at the moment t is obtained; deltaTUThe coefficient of the thermal power generating unit is the coefficient of the thermal power generating unit; omegaTUThe method is a thermal power generating unit set.
The power purchasing electric energy of the lower layer regional energy Internet is from the upper layer regional energy Internet, the power supply of the upper layer regional energy Internet mainly comprises new energy power generation, coal power generation and gas power generation, the ratio of different types of energy power generation in the power purchasing electric energy at different time periods is also changed, the permeability of different types of energy power generation is restricted,
Figure BDA0002825723190000205
wherein the content of the first and second substances,
Figure BDA0002825723190000206
respectively the permeability of different primary energy sources of renewable energy sources, coal-fired thermal power generating units and gas generating units in external electricity purchase at the time t,
Figure BDA0002825723190000211
the total amount of renewable energy power generation, the total amount of power generation of a coal-fired unit, the total amount of power generation of a gas unit and the total amount of power generation of all types of generator sets of the upper-layer energy internet power system at the time t are respectively.
And 4, considering the interactive layering characteristics of the lower-layer regional energy Internet and the upper-layer regional energy Internet, and sequentially performing an outer-layer cooperation game and an inner-layer cooperation game according to the lower-layer regional energy Internet energy efficiency optimization model and the upper-layer regional energy Internet economic optimization model to obtain an energy Internet optimization scheduling result.
The cooperative game is used for realizing multi-objective optimization, and the mathematical description of the multi-objective optimization problem is as follows:
Figure BDA0002825723190000212
wherein F (O) is a target function, and the number of F (O) is P; o, O,
Figure BDA0002825723190000213
ORespectively comprises q optimized variables, an upper limit of the optimized variables and a lower limit of the optimized variables；gi(O) is the ith inequality constraint, total kiA plurality of; h isj(O) is the jth equality constraint, ljA plurality of; f. of1(O)～fp(O) is a sub-targeting function; o1～oqVariables are optimized for children.
The mathematical description of the cooperative game method for solving the multi-objective optimization problem is as follows:
Figure BDA0002825723190000214
wherein the content of the first and second substances,
Figure BDA0002825723190000215
fi′(O*) Are respectively fi′The least desirable and most desirable values of (O).
The outer layer cooperation game is to carry out game by carrying out energy efficiency optimization on the lower layer area energy Internet and economic optimization on the upper layer energy Internet; the inner layer cooperation game is that the upper layer energy internet carries out the game of optimizing the economic cost of the electric power system and the economic cost of the gas system on the basis of the result of the outer layer cooperation game
The basic flow of the energy internet optimization scheduling is as follows: firstly, in an outer layer game, performing energy efficiency optimization on a lower layer region energy internet and economic optimization on an upper layer region energy internet to perform a game, and performing information interaction on game participants through an energy management system; determining the information of a connecting line after the outer game is finished; the upper-layer energy internet carries out power system economic cost optimization and gas system economic cost optimization game based on the determined tie line information which is the result of the outer-layer cooperation game, and game participants exchange power supply and gas supply output information through an energy management system; and fourthly, outputting an energy internet optimization scheduling result until the inner layer cooperation game is finished.
Step 5, outputting an energy internet optimized scheduling result to the lower-layer energy internet and the upper-layer energy internet; and outputting results such as workstations, servers, monitoring backgrounds, dispatching management centers and the like of a lower-layer energy internet manager and an upper-layer energy internet manager.
Taking the typical park (i.e. the lower zone) and the upper zone energy internet interactive layered architecture shown in fig. 2 as a simulation example, taking a typical winter day as a simulation period, the simulation step size is 1 hour, there is no cooling load demand in the typical winter day, and the ground source heat pump operates in the heating operation mode, so the cooling energy flow path in fig. 2 is not started temporarily.
The main parameter settings of the above method example: the environment temperature in the optimized scheduling period in the lower-layer park energy internet is set to be 269.15K; the heat energy loss factor of the gas turbine is 0.1; the efficiency of the waste heat recovery system is 0.82, and the working state of the waste heat recovery system and the gas turbine are matched, coordinated and combined to work together; the allowable working period of the ground source heat pump is 6: 00-24: 00, required downtime of 1: 00-5: 00; the maximum WT (total weight loss) rate and the maximum PV loss rate which can be accepted by the lower-layer park energy Internet are 60% and 60% according to the scheduling requirement; the lower limit of power exchange between the lower-layer park energy internet and the upper-layer energy internet electric energy connecting line is 5MW/h, the lower limit of power exchange between the natural gas connecting line is 15MW/h, electricity and gas are only allowed to be purchased from the upper-layer energy internet, and reverse power transmission is not allowed. The upper-layer energy Internet medium-voltage power generation units are 2 and are respectively abbreviated as TU #1 and TU # 2; the upward climbing rate and the downward climbing rate of the TU #1 are 50MW/h and 50MW/h respectively, the upper rotating reserve capacity is 50MW and the lower rotating reserve capacity is 50 MW; the ascending climbing rate and the descending climbing rate of TU #2 are respectively 20MW/h and 20MW/h, the upper rotating reserve capacity is 20MW and the lower rotating reserve capacity is 20 MW; the output of the air source is limited to 380 MW/h; the wind power abandonment penalty coefficient is set to be 30 $/MWh; the photovoltaic abandon penalty coefficient is set to 10 $/MWh.
And (4) analyzing results: the economic energy efficiency layering and collaborative day-ahead optimization scheduling results of the lower-layer park and the upper-layer energy internet are shown in table 1. Energy efficiency collaborative optimization scheduling of lower-layer park energy source internet
Figure BDA0002825723190000232
An efficiency maximization mode, on the basis of determining various types of loads of the energy Internet of the lower park, optimizing and solving
Figure BDA0002825723190000233
Efficiency maximum equivalent optimization solution input
Figure BDA0002825723190000234
And minimum. The cooperation game basis of the upper-layer area energy internet is the optimal compromise solution of the outer-layer cooperation game, in the upper-layer area energy internet, the economic cost target of the power system and the economic cost target of the natural gas system are subjected to inner-layer cooperation game, the power system and the natural gas system are different benefit complexes, the economic cost mainly considers the energy consumption cost of the respective energy system on the basis of meeting normal energy supply, and the inner-layer cooperation game enables the running economic cost of the area energy internet to be the lowest from the overall view of the upper-layer area energy internet.
According to table 1, in the outer layer cooperation game process, the optimal result of the energy efficiency collaborative optimization scheduling of the lower layer park energy internet is 0.4877294, the optimal result of the economic collaborative optimization scheduling of the upper layer energy internet is 332049.9$, and the optimal compromise solutions of the outer layer cooperation game are slightly inferior to the results of the corresponding single energy efficiency optimization and economic optimization. The result of the optimal compromise solution of the outer layer cooperative game is reduced by 0.8% relative to the efficiency optimization efficiency, is improved by 2.2% relative to the economic target value, and the amplitude is not large, but the optimal compromise solution of the cooperative game gives consideration to the two factors of the energy efficiency optimization and the economic optimization, so that the lower-layer park and the upper-layer energy Internet all obtain ideal satisfactory solutions.
Table 1 economic efficiency hierarchical collaborative optimization scheduling result under cooperative game
Figure BDA0002825723190000231
Figure BDA0002825723190000241
According to table 1, in the process of the inner layer game, both the cooperative game parties are economic cost targets, the cooperative game is relatively large in the optimal compromise solution of the game compared with the single economic cost optimization of the power system and the single economic cost optimization of the natural gas system, and the result of the optimal compromise solution balances the economic cost optimization of the power system and the economic cost of the natural gas system, so that both the game parties obtain a satisfactory solution.
The method comprehensively considers the interactive layering characteristics of the energy Internet, adopts the cooperative game to obtain the energy Internet optimized scheduling result, not only accords with the actual field of engineering application, is beneficial to reducing the optimized scheduling economic cost and improving the energy efficiency level, but also considers the pursuit of different layer power of different energy internets, and is beneficial to balancing the conflict and contradiction of benefits of all parties; meanwhile, the method considers multiple requirements of energy internet optimization scheduling from the aspects of economy and energy efficiency, and more meets benefit requirements of different optimization scheduling modes.
An energy internet optimization scheduling system based on cooperative game comprises,
an energy efficiency optimization model module: acquiring the information of the lower-layer regional energy Internet, and constructing a lower-layer regional energy Internet energy efficiency optimization model;
an economic optimization model module: collecting upper-layer energy internet information and constructing an upper-layer energy internet economic optimization model;
the cooperative game module: and (4) considering the interactive layering characteristics of the lower-layer regional energy Internet and the upper-layer regional energy Internet, and performing cooperative game according to the lower-layer regional energy Internet energy efficiency optimization model and the upper-layer regional energy Internet economic optimization model to obtain an energy Internet optimization scheduling result.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform a cooperative game-based energy internet optimization scheduling method.
A computing device comprising one or more processors, one or more memories, and one or more programs stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs including instructions for performing a cooperative game-based energy internet optimization scheduling method.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.

Claims (10)

1. An energy internet optimization scheduling method based on cooperative game is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
acquiring the information of the lower-layer regional energy Internet, and constructing a lower-layer regional energy Internet energy efficiency optimization model;
collecting upper-layer energy internet information and constructing an upper-layer energy internet economic optimization model;
and (4) considering the interactive layering characteristics of the lower-layer regional energy Internet and the upper-layer regional energy Internet, and performing cooperative game according to the lower-layer regional energy Internet energy efficiency optimization model and the upper-layer regional energy Internet economic optimization model to obtain an energy Internet optimization scheduling result.
2. The energy internet optimization scheduling method based on the cooperative game as claimed in claim 1, wherein: acquiring the information of the lower-layer regional energy Internet, constructing an energy conversion equipment model of the lower-layer regional energy Internet, and constructing a lower-layer regional energy Internet energy efficiency optimization model based on the energy conversion equipment model; the method comprises the steps of collecting upper-layer energy internet information, building an energy conversion equipment model of the upper-layer energy internet, and building an upper-layer energy internet economic optimization model based on the energy conversion equipment model.
3. The energy internet optimization scheduling method based on the cooperative game as claimed in claim 1 or 2, wherein: the energy efficiency optimization model of the lower-layer regional energy Internet is as follows,
an objective function:
Figure FDA0002825723180000011
wherein the content of the first and second substances,
Figure FDA0002825723180000012
is composed of
Figure FDA0002825723180000016
Efficiency;
Figure FDA0002825723180000013
for cooling load of output
Figure FDA0002825723180000017
The value, NT, is the optimized scheduling period, Δ t is the optimized scheduling simulation step length,
Figure FDA0002825723180000014
respectively the temperature of the cooling working environment and the temperature of a reference point,
Figure FDA0002825723180000015
is the cooling load at time t;
Figure FDA0002825723180000021
for the outgoing heating load
Figure FDA00028257231800000217
The value of the one or more of,
Figure FDA0002825723180000022
respectively as the temperature of the heating working environment and the temperature of a reference point,
Figure FDA0002825723180000023
is the thermal load at time t;
Figure FDA0002825723180000024
load of power supply for output
Figure FDA00028257231800000218
The value of the one or more of,
Figure FDA0002825723180000025
is the electrical load at time t;
Figure FDA0002825723180000026
for the supply of natural gas to the output
Figure FDA00028257231800000219
The value of the one or more of,
Figure FDA0002825723180000027
is the natural gas load at time t;
Figure FDA0002825723180000028
external network for input
Figure FDA00028257231800000220
The value of the one or more of,
Figure FDA0002825723180000029
respectively different primary energy source permeability eta of renewable energy, coal-fired thermal power generating unit and gas unit at t moment in external electricity purchasecoal、ηgasRespectively the working efficiency of a coal-fired thermal power generating unit and the working efficiency of a gas generating unit,
Figure FDA00028257231800000210
the purchased electric power at the time t;
Figure FDA00028257231800000211
for input of renewable energy
Figure FDA00028257231800000221
The value of the one or more of,
Figure FDA00028257231800000212
wind power generation power and photovoltaic power generation power at the moment t respectively;
Figure FDA00028257231800000213
for input of biomass
Figure FDA00028257231800000222
The value of the one or more of,
Figure FDA00028257231800000214
is the fuel consumption of the biomass boiler at the time t, ζbbBeing biomass
Figure FDA00028257231800000223
A factor;
Figure FDA00028257231800000215
for the input of natural gas
Figure FDA00028257231800000224
The value of the one or more of,
Figure FDA00028257231800000216
for the natural gas supply at time t, ζgOf natural gas
Figure FDA00028257231800000225
Factor(s)
Constraint conditions are as follows:
the output and climbing capacity of the energy conversion equipment, the operation constraint of the starting times in the optimized dispatching cycle,
Figure FDA0002825723180000031
wherein GT, GB, BB, GSHP, ECHI and ACHI represent energy conversion equipment units which are respectively a gas turbine, a gas boiler, a biomass boiler, a ground source heat pump, an electric refrigerator and an absorption refrigerator;
Figure FDA0002825723180000032
the method comprises the steps of obtaining a Boolean variable of the running state of the unit of the energy conversion equipment at the moment t;
Figure FDA0002825723180000033
the Boolean variable is the running state Boolean variable of the energy conversion equipment unit at the time t + 1; deltaunitThe cutter coefficient of the unit of the energy conversion equipment;
Figure FDA0002825723180000034
respectively the rated electric loading machine capacity, the hot loading machine capacity and the cold loading machine capacity of the unit of the energy conversion equipment;
Figure FDA0002825723180000035
respectively the electric power, the thermal power and the cold power of the energy conversion equipment unit at the time t;
Figure FDA0002825723180000036
respectively the electric power, the thermal power and the cold power of the energy conversion equipment unit at the moment t + 1;
Figure FDA0002825723180000037
the ramp rates under the electric power, the thermal power and the cold power of the energy conversion equipment unit are respectively;
Figure FDA0002825723180000038
are respectively asThe ascending rate of electric power, thermal power and cold power of the energy conversion equipment unit;
Figure FDA0002825723180000039
setting the upper limit of the starting times of the unit of the energy conversion equipment in the optimized scheduling period;
the minimum stop-and-start time constraints of the energy conversion equipment,
Figure FDA00028257231800000310
wherein the content of the first and second substances,
Figure FDA00028257231800000311
respectively the duration time of a starting state and the duration time of a stopping state of the energy conversion equipment unit at the time t-1;
Figure FDA00028257231800000312
respectively setting the minimum continuous starting time and the minimum continuous shutdown time of the unit of the energy conversion equipment;
Figure FDA00028257231800000313
the Boolean variable is the running state Boolean variable of the energy conversion equipment unit at the time t-1;
ground source heat pumps allow for operating time period operating constraints,
Figure FDA0002825723180000041
wherein, Tstart、TendRespectively setting the starting time and the ending time of the working time period of the ground source heat pump;
Figure FDA0002825723180000042
the Boolean variable is the running state Boolean variable of the ground source heat pump at the moment t; kappaGSHPThe maximum working time ratio of the ground source heat pump in the optimized dispatching cycle；
The waste heat recovery capability of the waste heat recovery device is restricted,
Figure FDA0002825723180000043
wherein the content of the first and second substances,
Figure FDA0002825723180000044
respectively recovering the residual heat quantity and the maximum residual heat recovery power of the residual heat recovery device at the moment t;
the output of the new energy is restricted,
Figure FDA0002825723180000045
wherein the content of the first and second substances,
Figure FDA0002825723180000046
the upper limit of the output of the wind power at the moment t is defined;
Figure FDA0002825723180000047
the upper limit of the output of the photovoltaic at the moment t is defined; sigmawt、σpvMaximum wind abandon rate and maximum light abandon rate are respectively allowed in the optimized scheduling period;
the energy balance is restrained by the constraint of energy balance,
Figure FDA0002825723180000048
wherein the content of the first and second substances,
Figure FDA0002825723180000051
electric power of the gas turbine at the time t;
Figure FDA0002825723180000052
the power consumptions of the electric refrigerator, the ice storage air conditioner and the ground source heat pump at the time t are respectively；
Figure FDA0002825723180000053
Respectively the thermal powers of a ground source heat pump, a biomass boiler and a gas boiler at the time t;
Figure FDA0002825723180000054
the recovered heat power of the ice storage air conditioner at the time t is obtained;
Figure FDA0002825723180000055
the cold powers of the electric refrigerator, the ice storage air conditioner, the ground source heat pump refrigeration and the absorption refrigerator at the moment t are respectively;
Figure FDA0002825723180000056
fuel input quantities of the gas turbine and the gas boiler at the time t are respectively;
Figure FDA0002825723180000057
is the natural gas supply at time t;
the tie-line power transfer limit constraints,
Figure FDA0002825723180000058
wherein the content of the first and second substances,P link、
Figure FDA0002825723180000059
respectively representing the lower limit of purchased electric power and the upper limit of purchased electric power at the time t;F link、
Figure FDA00028257231800000510
the lower limit of the natural gas power and the upper limit of the natural gas power are respectively at the moment t.
4. The energy internet optimization scheduling method based on the cooperative game as claimed in claim 1 or 2, wherein: the economic optimization model of the upper-layer energy Internet is that,
an objective function:
Figure FDA00028257231800000511
wherein the content of the first and second substances,
Figure FDA00028257231800000512
fre、fGW、fCHP、fTUrespectively calculating the running economic cost of the upper-layer energy internet, a new energy punishment economic cost function, an air source output economic cost function, a combined heat and power unit running economic cost function and a thermal power unit economic cost function, wherein NT is an optimized scheduling period, and delta t is an optimized scheduling simulation step length;
Figure FDA00028257231800000513
Figure FDA0002825723180000061
a polynomial coefficient of an economic cost function for the operation of the cogeneration unit,
Figure FDA0002825723180000062
respectively supplying the output electric power and the output thermal power of the cogeneration at the moment t;
Figure FDA0002825723180000063
πwt、πpvrespectively a wind curtailment penalty factor and a light curtailment penalty factor,
Figure FDA0002825723180000064
is the upper limit of the output of the wind power at the moment t,
Figure FDA0002825723180000065
the upper limit of the output of the photovoltaic at the moment t,
Figure FDA0002825723180000066
wind power generation power and photovoltaic power generation power at the moment t respectively;
Figure FDA0002825723180000067
Figure FDA0002825723180000068
is a polynomial coefficient of an economic cost function of the thermal power generating unit,
Figure FDA0002825723180000069
generating power of the thermal power generating unit at the moment t;
Figure FDA00028257231800000610
λGWis a natural gas price factor and is a natural gas price factor,
Figure FDA00028257231800000611
the output of the air source at the time t;
constraint conditions are as follows:
the rotational back-up constraint of the power system,
Figure FDA00028257231800000612
wherein the content of the first and second substances,
Figure FDA00028257231800000613
the standby capacity of the coal-fired thermal power generating unit in the upper-layer energy internet is respectively the upward rotation standby capacity and the downward rotation standby capacity at the moment t; SRup、SRdownRespectively upward rotation of power system in upper-layer energy internetRotating reserve capacity, rotating reserve capacity downward;
Figure FDA00028257231800000614
the ramp-up speed and the ramp-down speed of the thermal power generating unit are respectively set;
Figure FDA00028257231800000615
the installed capacity of the thermal power generating unit at the moment t is obtained; deltaTUThe coefficient of the thermal power generating unit is the coefficient of the thermal power generating unit; omegaTUThe method comprises the steps of (1) collecting thermal power generating units;
the power generation permeability of different types of energy is restricted,
Figure FDA0002825723180000071
wherein the content of the first and second substances,
Figure FDA0002825723180000072
respectively the permeability of different primary energy sources of renewable energy sources, coal-fired thermal power generating units and gas generating units in external electricity purchase at the time t,
Figure FDA0002825723180000073
the total amount of renewable energy power generation, the total amount of power generation of a coal-fired unit, the total amount of power generation of a gas unit and the total amount of power generation of all types of generator sets of the upper-layer energy internet power system at the time t are respectively.
5. The energy internet optimization scheduling method based on the cooperative game as claimed in claim 1, wherein: and considering the interactive layering characteristics of the lower-layer regional energy Internet and the upper-layer regional energy Internet, and sequentially performing an outer-layer cooperation game and an inner-layer cooperation game according to the lower-layer regional energy Internet energy efficiency optimization model and the upper-layer regional energy Internet economic optimization model to obtain an energy Internet optimization scheduling result.
6. The energy internet optimization scheduling method based on the cooperative game as claimed in claim 5, wherein: the outer layer cooperation game is to carry out game by carrying out energy efficiency optimization on the lower layer area energy Internet and economic optimization on the upper layer energy Internet;
the inner layer cooperation game is that the upper layer energy internet carries out the optimization game of the economic cost of the electric power system and the optimization game of the economic cost of the gas system on the basis of the result of the outer layer cooperation game.
7. The energy internet optimization scheduling method based on the cooperative game as claimed in claim 1, wherein: and outputting an energy internet optimized scheduling result to the lower-layer energy internet and the upper-layer energy internet.
8. The utility model provides an energy internet optimization scheduling system based on cooperation game which characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
an energy efficiency optimization model module: acquiring the information of the lower-layer regional energy Internet, and constructing a lower-layer regional energy Internet energy efficiency optimization model;
an economic optimization model module: collecting upper-layer energy internet information and constructing an upper-layer energy internet economic optimization model;
the cooperative game module: and (4) considering the interactive layering characteristics of the lower-layer regional energy Internet and the upper-layer regional energy Internet, and performing cooperative game according to the lower-layer regional energy Internet energy efficiency optimization model and the upper-layer regional energy Internet economic optimization model to obtain an energy Internet optimization scheduling result.
9. A computer readable storage medium storing one or more programs, characterized in that: the one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods of claims 1-7.
10. A computing device, characterized by: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
one or more processors, one or more memories, and one or more programs stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods of claims 1-7.
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