CN109861302B - Master-slave game-based energy internet day-ahead optimization control method - Google Patents

Master-slave game-based energy internet day-ahead optimization control method Download PDF

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CN109861302B
CN109861302B CN201811579903.4A CN201811579903A CN109861302B CN 109861302 B CN109861302 B CN 109861302B CN 201811579903 A CN201811579903 A CN 201811579903A CN 109861302 B CN109861302 B CN 109861302B
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power
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energy
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CN109861302A (en
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张有兵
王国烽
赵康莉
胡成鹏
卢俊杰
翁国庆
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Zhejiang University of Technology ZJUT
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
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Abstract

A method for optimizing and controlling energy Internet based on master-slave game includes initializing system and obtaining relevant parameters, setting initial internal price by network group control center, making decision by each ELN sub-network according to initial price to calculate out corresponding optimum strategy, integrating strategy set by network group control center, calculating out updated internal price by taking benefit maximization of network group control center as target, repeating said steps until game is balanced and internal price is not changed. And outputting the optimization strategy set at the moment as a day-ahead optimization result. The method can effectively improve the consumption capacity of the energy Internet to new energy and the system reliability in case of failure, and increases the economic benefit of the energy Internet to a certain extent.

Description

Master-slave game-based energy internet day-ahead optimization control method
Technical Field
The invention relates to an energy internet day-ahead optimization control method based on a master-slave game.
Background
Various national scholars combine intelligent power grid, microgrid and other power grid technologies and multidimensional interconnected internet technologies, an Energy internet concept of deep coupling of an Energy Network and an information Network is provided, and an Energy Local area Network (ELN) is taken as the most basic optimization and scheduling unit of the Energy internet, so that the Energy internet concept becomes a research hotspot in academia and industry.
The energy internet is a comprehensive energy system which deeply integrates electric power, natural gas, new energy and the like, and realizes interconnection of energy production, manufacture, storage, transmission and the like, so that optimal resource allocation is realized to the maximum extent. However, the energy internet has a lot of distributed devices, the data volume received and analyzed by the dispatching center is large, and the communication time is increased accordingly. And with the continuous access of large-scale distributed renewable energy sources to a power grid, the utilization of new energy sources has instability, fluctuation of load requirements and randomness of real-time electricity prices in most occasions, a large amount of uncertainty is introduced to a supply and demand side, and the output and the consumption of the whole system are unbalanced in power. The unbalanced problem of supply and demand side not only causes the energy extravagant, seriously influences the safe and stable operation of energy internet moreover. Therefore, the optimized operation management of the multi-energy system becomes the key for solving the problem of the energy Internet.
The traditional power grid adopts centralized control scheduling, and optimal scheduling can be realized only by acquiring global information through a central controller, so that the calculation amount is large, the reliability is poor, the investment cost is high, and the flexibility of energy transmission is lacked. With the coming of the energy internet era, the application of energy local area network group is increasingly wide. The energy local area network realizes real-time information sharing through bidirectional interaction at the supply and demand sides, so that interaction is carried out among the ELNs according to self conditions, real-time decision updating among the ELN groups is realized, the problem of unbalanced power is solved, and the safety and the reliability of the system are enhanced. And multi-energy complementary coordination is carried out among the subnetworks, so that the permeability of renewable energy is improved. In order to promote coordination and optimization among sources, loads and storages in an energy local area network, deeper research on energy internet optimization management is urgently needed.
Disclosure of Invention
In order to effectively complete the interconnection operation control of the energy local area network group and overcome the problem that the new energy utilization has instability in most occasions, the invention carries out the day-ahead optimization management scheme research on the energy Internet based on the master-slave game. The invention introduces a prediction control technology, establishes an energy local area network group model based on a principal and subordinate game theory, reduces uncertainty of prediction data, realizes reasonable configuration of energy and achieves maximum income.
In order to achieve the purpose, the technical scheme of the invention is as follows:
an energy internet optimization control method based on a master-slave game comprises the following steps:
s1: firstly, an energy local area network group model is constructed, a system is initialized, and parameters required by optimization, including day-ahead prediction data of wind energy, light energy and stored energy, are obtained;
s2: establishing a master-slave game model, taking a network group control center as a leader, setting an initial internal price by the leader, taking each ELN sub-network as a follower, making a decision according to the initial internal price, and calculating a corresponding optimal strategy;
s3: the network group control center integrates the strategy set of each ELN sub-network, and calculates the internal price again by taking the benefit maximization of the network group control center as a target, and the internal price is defined as the updated internal price;
s4: each ELN subnet carries out decision making according to the updated internal price, and an optimal strategy corresponding to the updated internal price is calculated;
s5: when the game reaches Stackelberg Equilibrium (SE) and the internal price is not updated any more, outputting a final optimization set as a previous optimization result of the energy local area network cluster;
s6: if the game does not reach the Stackelberg balance, returning to the step S2 to perform optimization again according to the updated state information.
In the invention, the energy Internet environment consists of a plurality of energy local area network individuals, the power supply side in each energy local area network individual consists of a photovoltaic, a fan, a gas turbine, an energy storage and other power grids, the demand side consists of a basic load and an electric refrigerator, the heat load is supplied by the gas turbine, a heat collector and a gas boiler, the cold load is supplied by the gas turbine and the electric refrigerator, and the whole ELN group is supplied or consumes electric energy by each ELN and the external power grid.
Further, in step S1, the system model includes the following components:
s1-1, basic load model: the ELN contains three types of loads, i.e., heat load, cold load, electrical load, and the model is as follows:
heat load: provided by a gas boiler, a heat exchanger and a heat collector:
Figure RE-GDA0001975331500000021
wherein the content of the first and second substances,
Figure RE-GDA0001975331500000022
is the thermal power of the gas boiler in the ELNi;
Figure RE-GDA0001975331500000023
is the heat power output by the heat exchanger;
Figure RE-GDA0001975331500000024
is the total power of the thermal load in the ELNi;
the thermal power output by the gas boiler is related to the fuel usage and the heat generation efficiency of the boiler.
Figure RE-GDA0001975331500000025
Figure RE-GDA0001975331500000031
Wherein the content of the first and second substances,
Figure RE-GDA0001975331500000032
η is the maximum thermal power of the gas boiler in ELNiGBIs the heat production efficiency of the gas boiler; vGB,iThe gas consumption of the boiler in a period of time; l isNGIs the heat value of natural gas and is 9.7kWh/m3；
The total gas consumption V of the system can be obtained according to the fuel consumption of the gas turbine and the gas boilerSUMComprises the following steps:
Figure RE-GDA0001975331500000033
wherein, VGT,iIs the natural gas consumption of the ELNi gas turbine during the period t;
the gas turbine is used as a main controllable energy supply device in the ELN system, not only provides electric energy for the ELN, but also recovers heat carried by high-temperature flue gas generated by the gas turbine by a heat recovery device, supplies heat for a heat load and supplies cold for a cold load through a heat exchanger and an absorption refrigeration device, and the relationship among the output, the heat and the gas consumption of the ELNi gas turbine is as follows:
Figure RE-GDA0001975331500000034
Figure RE-GDA0001975331500000035
Figure RE-GDA0001975331500000036
wherein the content of the first and second substances,
Figure RE-GDA0001975331500000037
generating power of the ELNi gas turbine for a period t;
Figure RE-GDA00019753315000000312
the maximum power generation power of the ELNi gas turbine;
Figure RE-GDA0001975331500000039
η waste heat recovery power of ELNi gas turbine in t periodcAnd ηrThe generating efficiency and the waste heat recovery efficiency of the ELNi gas turbine are achieved;
Figure RE-GDA00019753315000000310
is the natural gas consumption of the ELNi gas turbine during the period t;
further, the power generation efficiency and the heat recovery efficiency of the gas turbine are greatly affected by the unit load factor, and the relationship between these two and the unit load factor β is as follows:
Figure RE-GDA00019753315000000311
Figure RE-GDA0001975331500000041
0.25≤β≤1 (10)
in the formula (I), the compound is shown in the specification,
Figure RE-GDA0001975331500000042
the rated power generation efficiency of the gas turbine;
Figure RE-GDA00019753315000000418
β is the unit load factor;
the heat exchanger exchanges a part for heating in the waste heat recovered from the gas turbine with water to obtain output thermal power;
Figure RE-GDA0001975331500000044
in the formula (I), the compound is shown in the specification,
Figure RE-GDA00019753315000000419
is that
Figure RE-GDA00019753315000000420
A portion assigned for heating ηHXThe heat exchange efficiency of the heat exchanger;
the cold load is provided by the absorption refrigeration device and the electric refrigerator:
Figure RE-GDA0001975331500000047
wherein the content of the first and second substances,
Figure RE-GDA0001975331500000048
is the total power of the cold load in the ELNi;
Figure RE-GDA0001975331500000049
the cold power output by the absorption refrigeration device is the cold power output by the absorption refrigeration device;
Figure RE-GDA00019753315000000410
the refrigeration power of the electric refrigerator is ELNi;
the absorption refrigeration device provides a part of the waste heat for refrigeration to a heat exchanger in the device, so that the device converts cold energy;
Figure RE-GDA00019753315000000411
in the formula (I), the compound is shown in the specification,
Figure RE-GDA00019753315000000412
is that
Figure RE-GDA00019753315000000413
Middle portion allocated for cooling ηACThe refrigeration efficiency of the absorption refrigeration device;
the electric refrigerator is a special load for generating a cooling load, and can be adjusted, and belongs to a determined party under the condition that the cooling load is known, and the output of the electric refrigerator of the ith ELN meets the following requirements:
Figure RE-GDA00019753315000000414
Figure RE-GDA00019753315000000415
wherein the content of the first and second substances,
Figure RE-GDA00019753315000000416
input power of electric refrigerator being ELNi ηECThe refrigeration efficiency of the electric refrigerator;
Figure RE-GDA00019753315000000417
maximum input power of the electric refrigerator of ELNi;
electrical loading: the electric load is mainly divided into two types, namely a basic load and an electric refrigerator according to whether the electric load is related to other two types or not, and the basic load model is as follows:
for ELNi ∈ I, the base load is as follows:
Figure RE-GDA0001975331500000051
wherein the content of the first and second substances,
Figure RE-GDA0001975331500000052
the method is the basic load of ELNi under the condition of adopting the electricity price of an external power grid; lambda [ alpha ]bRepresents the price of electrical energy purchased from an external power grid; lambda [ alpha ]sFor sale to external power gridPrice of electric energy, rbRepresenting the internal purchase price, r, of the ELN groupsRepresenting the internal electricity selling price of the ELN group, the electricity price should satisfy the following constraint:
λs≤rs＜rb≤λb(17)
s1-2. energy storage system model
The energy storage system reduces the net load of a single ELN and the whole ELN group through two controllable operations of charging and discharging, the SoC of each time period is related to the charging and discharging state and the charging and discharging amount of the previous time period, and in the t time period, the working model of the energy storage system of the ith ELN is as follows:
Figure RE-GDA0001975331500000053
Figure RE-GDA0001975331500000054
in the formula (I), the compound is shown in the specification,
Figure RE-GDA0001975331500000055
the energy stored by the ELNi energy storage system is t time period;
Figure RE-GDA0001975331500000056
residual capacity of ELNi energy storage in t period; qBES,iThe total capacity of the ELNi energy storage system;
Figure RE-GDA0001975331500000059
the charging power of the ELNi energy storage system is t time period;
Figure RE-GDA0001975331500000058
η discharge power of ELNi energy storage system in t periodchAnd ηdchThe charging efficiency and the discharging efficiency of the energy storage system are obtained;
moreover, the energy storage system in the ELN still needs to constrain the charging and discharging power of itself and the state of SoC, and simultaneously satisfies the requirement that the SoC state is not changed before and after operating one day:
Figure RE-GDA0001975331500000062
Figure RE-GDA0001975331500000063
in the formula (I), the compound is shown in the specification,
Figure RE-GDA0001975331500000064
and
Figure RE-GDA0001975331500000065
respectively representing the charge and discharge power of the ELNi energy storage system;
Figure RE-GDA0001975331500000066
and
Figure RE-GDA0001975331500000067
the maximum charge-discharge power of the ELNi energy storage system;
Figure RE-GDA0001975331500000068
and
Figure RE-GDA0001975331500000069
representing the charge-discharge state of the ELNi energy storage system, and taking 0 or 1, wherein 0 represents that the ELNi energy storage system is not in the charge-discharge state, and 1 represents that the ELNi energy storage system is in the charge-discharge state;
Figure RE-GDA00019753315000000610
representing the charge and discharge power of the ELN i energy storage system in a t period;
Figure RE-GDA00019753315000000611
the residual capacity of the ELNi energy storage system is the upper and lower limits;
s1-3 renewable energy model
The photovoltaic system has a maximum power point tracking function, can be adjusted according to the illumination intensity and the ambient temperature, tracks and outputs the maximum power in the time period, and the output of the photovoltaic system of the ELNi is as follows:
Figure RE-GDA00019753315000000612
in the formula, Ppv,iAverage value of all photovoltaic system power;
the fan system converts the mechanical energy of wind into electric energy, the output power fluctuates along with the change of the local average wind speed in the time period, and the output of the ELNi fan system is as follows:
Figure RE-GDA00019753315000000613
in the formula, PWT,iThe average value of all fan system power is obtained;
s1-4.ELN Power balance model
Obtaining an electric power balance model of an ith ELN through various components of the ELN on a power supply side and a demand side:
Figure RE-GDA00019753315000000614
Figure RE-GDA0001975331500000071
wherein the content of the first and second substances,
Figure RE-GDA0001975331500000072
the exchange power of ELNi and the network group;
Figure RE-GDA0001975331500000073
is a transmission line power constraint.
Still further, the master-slave game model in step S2 is established as follows:
s2-1. yield model of Single ELN
The effect of the power balance is to reduceThe net load of the ELN, here set to power balance, exchanges power with the ELN tie
Figure RE-GDA0001975331500000074
The profit of the electric energy transaction is the profit of the transaction within the grid, the electricity purchase price and the electricity sale price between the grid control center and each ELN sub-network are used, and the profit is the profit when the exchange power on the ELN connection is positive
Figure RE-GDA0001975331500000075
Otherwise, the benefit is
Figure RE-GDA0001975331500000076
The benefit of gas consumption is expressed as the inverse of the gas consumption cost;
thus, the utility function for ELN is obtained as:
Figure RE-GDA0001975331500000077
where ρ is the correlation coefficient of the power balance utility, and is in order of magnitude
Figure RE-GDA0001975331500000078
The same; r isNGIs the gas price;
s2-2. revenue model of ELN cluster control center
Since the ELN in the system performs power interaction through one bus and performs energy interaction with an external power grid through the bus, certain constraint is provided for the power of the bus;
Figure RE-GDA0001975331500000079
Figure RE-GDA00019753315000000710
the network group control center is a mechanism manually set in the system, is used as a leader of a master-slave game model, and has the optimization goal of maximizing the benefits of the network group control center, and the revenue function of the network group control center set for the purpose is as follows:
Figure RE-GDA00019753315000000711
wherein the content of the first and second substances,
Figure RE-GDA00019753315000000712
and
Figure RE-GDA00019753315000000713
the sum of power is exchanged for all ELNs purchasing and selling electricity during the time period t.
In step S3, the master-slave game is implemented as follows:
the network group control center provides an initial internal price, each ELN is simulated to respond to the initial internal price to obtain the determined electric load, local optimization is carried out according to the utility function of each ELN to obtain all optimal strategies of each ELN, and then the interactive power u of each ELN and the network group control center is obtainedgrid. The network group control center optimizes the optimization with the maximization of the benefit as the target according to the reaction of each ELN to the initial internal electricity price and the influence of the internal electricity price on the electric loadbAnd rsThe network group control center uses the value as the initial value of the next optimization to carry out iteration, and finally the optimal r is obtainedbAnd rsAnd accordingly obtaining a scheduling scheme of each ELN;
the game model thus formed is as follows:
L＝{(I∪{GM}),{PLoad,i}i∈I,{Rb},{Rs},{Ui}i∈I,R} (31)
the composition comprises the following components:
1) the set I of the ELNs is a follower, and a response network group control center GM is used as an internal interaction electricity price set by the leader;
2)PLoad,iis a strategy set of ELN i for adjusting load and the corresponding variable PLoad,iContaining constraints
Figure RE-GDA0001975331500000081
3)Rb、RsIs a policy set of GM, corresponding to decision variable r of GMbAnd rs；
4)UiFor the yield function of ELNi, the upper layer optimization can be performed by the variable PLoad,iAnd constraints (5) - (11), (14) - (15), (17) - (21);
5) and R is a yield function of the GM, and the effect is to obtain the profit of energy interaction in the ELN group and trade with an external power grid.
In step S5, the Stackelberg equalization is implemented as follows:
based on the master-slave game model in the step S3, the cluster control center determines the optimal price through the optimal reaction of each ELN, each ELN determines the optimal value of its own decision on the basis of the price, and the mode for achieving the above scheme through the master-slave game is Stackelberg balance (SE);
in the Stackelberg game L defined in (31), when a policy set is in use
Figure RE-GDA0001975331500000082
Is composed of
Figure RE-GDA0001975331500000091
Wherein, for
Figure RE-GDA0001975331500000092
Are all provided with
Figure RE-GDA0001975331500000093
Namely, it is
Figure RE-GDA0001975331500000094
Is composed of
Figure RE-GDA0001975331500000095
A set of (a); while
Figure RE-GDA0001975331500000096
Therefore, when the above condition is satisfied, namely the game reaches SE, the SE electricity price
Figure RE-GDA0001975331500000097
And
Figure RE-GDA0001975331500000098
and load demand at SE
Figure RE-GDA0001975331500000099
And (4) uniquely determining.
In step S6, it is determined whether the updated internal electricity prices maximize the revenue of the grid control center, and if the updated internal prices do not change any more, a final optimization strategy set is output as a result of the day-ahead optimization of the energy local area network cluster, otherwise, the step S2 is skipped to perform the optimization again.
The invention has the beneficial effects that:
1. the interconnection control of the energy local area network group is completed, the reasonable configuration of energy and the construction of the energy internet group are realized, the consumption of renewable energy is promoted, and the reliability of a power grid is enhanced.
2. The method has the advantages that the load demand is guaranteed, the output plans of multiple energy sources and stored energy in the ELN sub-network and the change of internal real-time electricity prices are coordinated, the output of new energy sources is tracked in time, the net load fluctuation is gentle, the ELN group power is balanced, and the running stability of the system is effectively improved.
3. The optimization method has stronger robustness under the condition that the prediction data has uncertainty, can effectively relieve the influence of instability and uncertainty of the system, and ensures the effective implementation of a scheduling plan and the stable operation of the system.
Drawings
FIG. 1 is an ELN cluster system architecture.
Fig. 2 is a schematic diagram of a master-slave gaming method.
FIG. 3 is a net load curve for the ELN population in 3 modes.
FIG. 4 is a graph of the total objective function and total cost of the web farm in an iterative process.
FIG. 5 is a graph of the change in net group payload fluctuation rate over an iterative process.
Fig. 6 is a relationship between the total cost of the ELN group, the net load fluctuation rate, and the power balance coefficient ρ.
Fig. 7 is a flow chart of a master-slave game-based energy internet day-ahead optimization control method.
Detailed description of the invention
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1 to 7, an energy internet day-ahead optimization control method based on a master-slave game includes the following steps:
s1: firstly, an energy local area network group model is constructed, a system is initialized, and parameters required by optimization, including day-ahead prediction data of wind energy, light energy and stored energy, are obtained;
s2: establishing a master-slave game model, taking a network group control center as a leader, setting an initial internal price by the leader, taking each ELN sub-network as a follower, making a decision according to the initial internal price, and calculating a corresponding optimal strategy;
s3: the network group control center integrates the strategy set of each ELN sub-network, and calculates the internal price again by taking the benefit maximization of the network group control center as a target, and the internal price is defined as the updated internal price;
s4: each ELN subnet carries out decision making according to the updated internal price, and an optimal strategy corresponding to the updated internal price is calculated;
s5: when the game reaches Stackelberg Equilibrium (SE) and the internal price is not updated any more, outputting a final optimization set as a previous optimization result of the energy local area network cluster;
s6: if the game does not reach the Stackelberg balance, returning to the step S2 to perform optimization again according to the updated state information.
In the invention, the energy Internet environment consists of a plurality of energy local area network individuals, the power supply side in each energy local area network individual consists of a photovoltaic, a fan, a gas turbine, an energy storage and other power grids, the demand side consists of a basic load and an electric refrigerator, the heat load is supplied by the gas turbine, a heat collector and a gas boiler, the cold load is supplied by the gas turbine and the electric refrigerator, and the whole ELN group is supplied or consumes electric energy by each ELN and the external power grid.
Further, in step S1, the system model includes the following components:
s1-1, basic load model: the ELN contains three types of loads, i.e., heat load, cold load, electrical load, and the model is as follows:
heat load: provided by a gas boiler, a heat exchanger and a heat collector:
Figure RE-GDA0001975331500000101
wherein the content of the first and second substances,
Figure RE-GDA0001975331500000105
is the thermal power of the gas boiler in the ELNi;
Figure RE-GDA0001975331500000103
is the heat power output by the heat exchanger;
Figure RE-GDA0001975331500000104
is the total power of the thermal load in the ELNi;
the thermal power output by the gas boiler is related to the fuel consumption and the heat production efficiency of the boiler;
Figure RE-GDA0001975331500000111
Figure RE-GDA0001975331500000112
wherein the content of the first and second substances,
Figure RE-GDA0001975331500000113
η is the maximum thermal power of the gas boiler in ELNiGBIs the heat production efficiency of the gas boiler; vGB,iThe gas consumption of the boiler in a period of time; l isNGIs the heat value of natural gas and is 9.7kWh/m3；
The total gas consumption V of the system can be obtained according to the fuel consumption of the gas turbine and the gas boilerSUMComprises the following steps:
Figure RE-GDA0001975331500000114
wherein, VGT,iIs the natural gas consumption of the ELNi gas turbine during the period t;
the gas turbine as the controllable energy supply device in the ELN system not only provides electric energy for the ELN, but also can recover the heat carried by the high-temperature flue gas generated by the gas turbine by the heat recovery device, and supplies heat for a heat load and supplies cold for a cold load through the heat exchanger and the absorption type refrigerating device, and the relationship among the output, the heat and the gas consumption of the ELNi gas turbine is as follows:
Figure RE-GDA0001975331500000115
Figure RE-GDA0001975331500000116
Figure RE-GDA0001975331500000117
wherein the content of the first and second substances,
Figure RE-GDA0001975331500000118
generating power of the ELNi gas turbine for a period t;
Figure RE-GDA0001975331500000119
the maximum power generation power of the ELNi gas turbine;
Figure RE-GDA00019753315000001110
η waste heat recovery power of ELNi gas turbine in t periodcAnd ηrThe generating efficiency and the waste heat recovery efficiency of the ELNi gas turbine are achieved;
Figure RE-GDA00019753315000001111
is the natural gas consumption of the ELNi gas turbine during the period t;
in addition, the power generation efficiency and the waste heat recovery efficiency of the gas turbine are greatly influenced by the unit load factor, and the relationship between the power generation efficiency and the waste heat recovery efficiency and the unit load factor beta can be found according to relevant documents as follows:
Figure RE-GDA0001975331500000121
Figure RE-GDA0001975331500000122
0.25≤β≤1 (10)
in the formula (I), the compound is shown in the specification,
Figure RE-GDA0001975331500000123
the rated power generation efficiency of the gas turbine;
Figure RE-GDA0001975331500000124
β is the unit load factor;
the heat exchanger 3 exchanges a portion for heating among the waste heat recovered from the gas turbine with water to obtain an output thermal power.
Figure RE-GDA0001975331500000125
In the formula (I), the compound is shown in the specification,
Figure RE-GDA00019753315000001219
is that
Figure RE-GDA0001975331500000127
A portion assigned for heating ηHXThe heat exchange efficiency of the heat exchanger;
the cold load is provided by the absorption refrigeration device and the electric refrigerator:
Figure RE-GDA0001975331500000128
wherein the content of the first and second substances,
Figure RE-GDA0001975331500000129
is the total power of the cold load in the ELNi;
Figure RE-GDA00019753315000001210
the cold power output by the absorption refrigeration device is the cold power output by the absorption refrigeration device;
Figure RE-GDA00019753315000001211
the refrigeration power of the electric refrigerator is ELNi;
absorption refrigeration device: the absorption refrigeration device provides a part of the waste heat for refrigeration to a heat exchanger in the device, so that the device converts cold energy;
Figure RE-GDA00019753315000001212
in the formula (I), the compound is shown in the specification,
Figure RE-GDA00019753315000001213
is that
Figure RE-GDA00019753315000001214
Middle portion allocated for cooling ηACThe refrigeration efficiency of the absorption refrigeration device;
the electric refrigerator is a special load for generating a cooling load, and can be adjusted, and belongs to a determined party under the condition that the cooling load is known, and the output of the electric refrigerator of the ith ELN meets the following requirements:
Figure RE-GDA00019753315000001215
Figure RE-GDA00019753315000001216
wherein the content of the first and second substances,
Figure RE-GDA00019753315000001217
input power of electric refrigerator being ELNi ηECThe refrigeration efficiency of the electric refrigerator;
Figure RE-GDA00019753315000001218
maximum input power of the electric refrigerator of ELNi;
electrical loading: the electric load is mainly divided into two types, namely a basic load and an electric refrigerator according to whether the electric load is related to other two types or not, and the basic load model is as follows:
for ELNi ∈ I, the base load is as follows:
Figure RE-GDA0001975331500000131
wherein the content of the first and second substances,
Figure RE-GDA0001975331500000132
the method is the basic load of ELNi under the condition of adopting the electricity price of an external power grid; lambda [ alpha ]bRepresents the price of electrical energy purchased from an external power grid; lambda [ alpha ]sRepresenting the price of electricity sold to an external grid, rbRepresenting the internal purchase price, r, of the ELN groupsRepresenting the internal electricity selling price of the ELN group, the electricity price should satisfy the following constraint:
λs≤rs＜rb≤λb(17)
s1-2. energy storage system model
The energy storage system reduces the net load of a single ELN and the whole ELN group through two controllable operations of charging and discharging, the SoC of each time period is related to the charging and discharging state and the charging and discharging amount of the previous time period, and in the t time period, the working model of the energy storage system of the ith ELN is as follows:
Figure RE-GDA0001975331500000133
Figure RE-GDA0001975331500000134
in the formula (I), the compound is shown in the specification,
Figure RE-GDA0001975331500000139
the energy stored by the ELNi energy storage system is t time period;
Figure RE-GDA0001975331500000136
residual capacity of ELNi energy storage in t period; qBES,iThe total capacity of the ELNi energy storage system;
Figure RE-GDA0001975331500000137
the charging power of the ELNi energy storage system is t time period;
Figure RE-GDA0001975331500000138
η discharge power of ELNi energy storage system in t periodchAnd ηdchThe charging efficiency and the discharging efficiency of the energy storage system are obtained;
moreover, the energy storage system in the ELN still needs to constrain the charging and discharging power of itself and the state of SoC, and simultaneously satisfies the requirement that the SoC state is not changed before and after operating one day:
Figure RE-GDA0001975331500000141
Figure RE-GDA0001975331500000142
Figure RE-GDA0001975331500000143
in the formula (I), the compound is shown in the specification,
Figure RE-GDA0001975331500000144
and
Figure RE-GDA0001975331500000145
respectively show an ELNi energy storage systemThe charging and discharging power of (1);
Figure RE-GDA0001975331500000146
and
Figure RE-GDA0001975331500000147
the maximum charge-discharge power of the ELNi energy storage system;
Figure RE-GDA0001975331500000148
and
Figure RE-GDA0001975331500000149
representing the charge-discharge state of the ELNi energy storage system, and taking 0 or 1, wherein 0 represents that the ELNi energy storage system is not in the charge-discharge state, and 1 represents that the ELNi energy storage system is in the charge-discharge state;
Figure RE-GDA00019753315000001410
representing the charge and discharge power of the ELN i energy storage system in a t period;
Figure RE-GDA00019753315000001411
the residual capacity of the ELNi energy storage system is the upper and lower limits;
s1-3 renewable energy model
The photovoltaic system has a maximum power point tracking function, can be adjusted according to the illumination intensity and the ambient temperature, tracks and outputs the maximum power in the time period, and the output of the photovoltaic system of the ELNi is as follows:
Figure RE-GDA00019753315000001412
in the formula, Ppv,iAverage value of all photovoltaic system power;
the fan system converts the mechanical energy of wind into electric energy, the output power fluctuates along with the change of the local average wind speed in the time period, and the output of the ELNi fan system is as follows:
Figure RE-GDA00019753315000001413
in the formula, PWT,iThe average value of all fan system power is obtained;
s1-4.ELN Power balance model
Obtaining an electric power balance model of an ith ELN through various components of the ELN on a power supply side and a demand side:
Figure RE-GDA00019753315000001414
Figure RE-GDA0001975331500000151
wherein the content of the first and second substances,
Figure RE-GDA0001975331500000152
the exchange power of ELNi and the network group;
Figure RE-GDA0001975331500000153
is a transmission line power constraint.
Still further, the master-slave game model in step S2 is established as follows:
s2-1. yield model of Single ELN
The role of power balancing is to reduce the net load of the ELN, where power balancing is set to exchange power with the ELN link
Figure RE-GDA0001975331500000154
The square of the electric energy is related, the income of the electric energy transaction is the income of the transaction in the network group, and the electricity purchasing price and the electricity selling price between the network group control center and each ELN sub-network are required to be used; furthermore, when the exchange power on the ELN link is positive, the gain is
Figure RE-GDA0001975331500000155
Otherwise, the benefit is
Figure RE-GDA0001975331500000156
The benefit of gas consumption is expressed as the inverse of the gas consumption cost;
thus, the utility function for ELN is obtained as:
Figure RE-GDA0001975331500000157
where ρ is the correlation coefficient of the power balance utility, and is in order of magnitude
Figure RE-GDA0001975331500000158
The same; r isNGIs the gas price;
s2-2. revenue model of ELN cluster control center
Since the ELN in the system performs power interaction through one bus and performs energy interaction with an external power grid through the bus, certain constraint is provided for the power of the bus;
Figure RE-GDA0001975331500000159
Figure RE-GDA00019753315000001510
the network group control center is a mechanism manually set in the system, is used as a leader of a master-slave game model, and has the optimization goal of maximizing the benefits of the network group control center, and the revenue function of the network group control center set for the purpose is as follows:
Figure RE-GDA00019753315000001511
wherein the content of the first and second substances,
Figure RE-GDA00019753315000001512
and
Figure RE-GDA00019753315000001513
the sum of power is exchanged for all ELNs purchasing and selling electricity during the time period t.
In step S3, the master-slave game is implemented as follows:
the network group control center provides an initial internalSimulating each ELN to respond to the initial internal price to obtain the determined electric load, and carrying out local optimization according to the utility function of each ELN to obtain all optimal strategies of each ELN, thereby obtaining the interactive power u of each ELN and the network group control centergridThe network group control center considers the influence of the internal electricity price on the electric load according to the reaction of each ELN to the initial internal electricity price and optimizes the reaction by taking the maximization of the benefit of the network group control center as a target, thereby reasonably setting rbAnd rs. The network group control center uses the value as the initial value of the next optimization to carry out iteration, and finally the optimal r is obtainedbAnd rsAnd obtaining a scheduling scheme of each ELN according to the scheduling scheme;
the game model thus formed is as follows:
L＝{(I∪{GM}),{PLoad,i}i∈I,{Rb},{Rs},{Ui}i∈I,R} (31)
the composition comprises the following components:
1) and the set I of the ELNs is a follower, and the response network group control center GM is used as an internal interactive electricity price set by the leader.
2)PLoad,iIs a strategy set of ELN i for adjusting load and the corresponding variable PLoad,iContaining constraints
Figure RE-GDA0001975331500000161
3)Rb、RsIs a policy set of GM, corresponding to decision variable r of GMbAnd rs。
4)UiFor the yield function of ELNi, the upper layer optimization can be performed by the variable PLoad,iAnd constraints (5) - (11), (14) - (15), (17) - (21).
5) And R is a yield function of the GM, and the effect is to obtain the profit of energy interaction in the ELN group and trade with an external power grid.
In step S5, the Stackelberg equalization is implemented as follows:
based on the master-slave game model in step S3, the cluster control center determines the optimal price through the optimal reaction of each ELN, and each ELN determines the optimal value of its own decision based on the price. The mode of achieving the scheme through the master-slave game is Stackelberg balance (SE);
in the Stackelberg game L defined in (31), when a policy set is in use
Figure RE-GDA0001975331500000162
Is composed of
Figure RE-GDA0001975331500000171
Wherein, for
Figure RE-GDA0001975331500000172
Are all provided with
Figure RE-GDA0001975331500000173
Namely, it is
Figure RE-GDA0001975331500000174
Is composed of
Figure RE-GDA0001975331500000175
A set of (a); while
Figure RE-GDA0001975331500000176
Therefore, when the above condition is satisfied, namely the game reaches SE, the SE electricity price
Figure RE-GDA0001975331500000177
And
Figure RE-GDA0001975331500000178
and load demand at SE
Figure RE-GDA0001975331500000179
And (4) uniquely determining.
In step S6, it is determined whether the updated internal electricity prices maximize the revenue of the grid control center, and if the updated internal prices do not change any more, a final optimization strategy set is output as a result of the day-ahead optimization of the energy local area network cluster, otherwise, the step S2 is skipped to perform the optimization again.
To better understand the benefits of the present invention to those skilled in the art, applicants analyzed and compared the ELN population net load characteristics and economics for three modes of operation. The effectiveness of the energy optimization management method is verified by taking an ELN group consisting of 4 ELNs of a certain place as an example, wherein the ELN1 comprises cold and hot loads; ELN2 contains thermal load; ELN3 includes cold and cold load and cold-and-hot-load-free ELN4 for simulating various ELN structures. The three operating modes are as follows:
mode 1: before optimization, the power generation equipment generates power at full load, and the stored energy does not participate in a dispatching mode.
Mode 2: the internal electricity prices are the same as the external electricity prices and a non-cooperative gaming mode is employed.
Mode 3: the invention uses a real-time electricity price mode based on the Stackelberg master-slave game.
The statistics associated with the payload characteristics are shown in table 1, where RPR is the relative pre-optimization surge reduction rate. Combining the net load graph 3 with table 1, it can be seen that the mode 3 is reduced by 82.44% and 29.22% in peak-to-valley difference, and reduced by 80.05% and 27.08% in fluctuation rate, compared with the modes 1 and 2, respectively, and the power margin of the ELN group system is reduced, and the stability of the system is improved. In addition, compared with the mode 1, the energy utilization rates of the modes 2 and 3 are obviously improved and reach or approach the degree of full utilization of energy.
Mode(s) Peak to valley difference/kW Fluctuation ratio/kW RPR Energy utilization rate
1 8701.1 1894.4 —— 0.9537
2 2158.1 518.4 0.7397 1
3 1527.6 378.0 0.7939 0.9996
TABLE 1
According to the economic calculation model, the economic-related statistical data can be obtained as shown in table 2.
Figure RE-GDA0001975331500000181
TABLE 2
Analyzing the data in table 2 and fig. 4 can lead to the following conclusions:
1) mode 2, mode 1 before 3 optimization relatively, the energy utilization ratio of fan, photovoltaic has obtained obvious promotion, abandons the scene loss and all reduces to near zero, and the photoelectricity subsidy has promoted 4.85% and 4.82% respectively, but simultaneously because there is the energy storage also participated in the accommodation process, has appeared charge-discharge loss, electric energy loss in the cost, and the fortune dimension expense also promotes slightly. In addition, the charge-discharge loss, the electric energy loss and the operation and maintenance cost of the mode 3 are slightly improved compared with those of the mode 2, which shows that the energy storage scheduling degree in the optimization process is higher, and the economic impact is small.
2) The total cost of the 3 modes is negative, which indicates that the system is stable and profitable. The mode 3 improves the total yield by 290.06% and 123.31% respectively compared with the modes 1 and 2, because the total cost before optimization is relatively large and the cost is too small, the improvement is very obvious, and meanwhile, the main-slave game optimization process used by the invention obviously improves the economy of the ELN group.
3) Compared with the mode 1, the natural gas consumption of the modes 2 and 3 is respectively increased by 11.01% and 4.52%, the average unit power generation cost of the gas turbine is respectively increased by 19.92% and 15.11%, which shows that the optimization processes of the modes 2 and 3 can reduce the power generation power of the gas turbine set in certain periods, and other modes of cooling and heating are adopted, so that the natural gas consumption is increased, but the mode 3 used by the invention is less increased, and the effectiveness of the optimization of the electricity price set by the grid group control center on the operation of each ELN system is reflected.
In the simulation process of the day-ahead plan, the selected CPLEX solver has the limitation of solving conditions, so that the master-slave game cannot be written into the same solving process. Therefore, the game needs to be iterated to obtain the final optimization effect. If the power balance coefficient ρ is set to 200, the iterative process of the system is as shown in fig. 5. Wherein, the total objective function of the net group refers to the sum of all response main body objective functions, which is
Figure RE-GDA0001975331500000191
The total cost of a net cluster is the sum of all ELN costs within the cluster
In the iteration process, with the increase of the iteration times, the total objective function and the total cost of the ELN group are monotonically decreased, the sum of the net load variances of the ELN group also shows a decreasing trend, and the net load variances of the ELN group are converged to a constant value finally when the iteration times are 12, so that the Stackelberg balance is achieved. Under the operation state corresponding to the value, the ELN group can simultaneously achieve the minimum of the total net load fluctuation and the best in economy.
In addition, the relationship between the power balance coefficient ρ and the ELN group optimization result in the ELN profit model is also explored, and the obtained relationship is shown in fig. 6. When rho is more than 1500, the total cost of the ELN group is fluctuated with the increase of rho but is basically unchanged, and when rho is more than 1500, the total cost of the ELN group is sharply increased with the increase of rho; on the other hand, the ELN group net load fluctuation rate takes a minimum value around ρ 200, and decreases with ρ when ρ < 200 and increases with ρ when ρ > 200 lead to an increase in net load fluctuation rate. In summary, when ρ is about 200, the system can reach the optimal operation state of economy and stability.
The invention relates to an ELN group system considering interactive response, which is used for modeling various devices related to various energy sources in the ELN and designing an internal real-time electricity price model based on a master-slave game aiming at the interactive response among the ELNs on the basis. The invention completes the ELN group energy optimization management method on the basis of the models so as to find the optimal economic operation strategy of the ELN group.
The method coordinates output plans of controllable sources, charges and storages in each ELN and changes of internal real-time electricity prices, simultaneously promotes the ELN group to approach power balance overall, and obviously improves the stability of each ELN and the ELN group system. Compared with a system model with unchanged electricity price, the real-time electricity price system model can reduce the consumption cost of natural gas to a greater extent at the cost of slightly increasing the loss and operation and maintenance cost in the scheduling process, thereby greatly improving the overall economy.
In the description of the present specification, case comparison and analysis, random scene analysis, and the like are used to describe specific features, structures, and benefits of the present invention. In this specification, the schematic representations of the invention are not necessarily directed to the same embodiments or examples, and those skilled in the art may combine and combine various embodiments or examples described in this specification. In addition, the embodiments described in this specification are merely illustrative of implementation forms of the inventive concept, and the scope of the present invention should not be construed as being limited to the specific forms set forth in the implementation examples, but also includes equivalent technical means which can be conceived by those skilled in the art according to the inventive concept.

Claims (5)

1. An energy internet optimization control method based on a master-slave game is characterized by comprising the following steps:
s1: firstly, an energy local area network group model is constructed, a system is initialized, and parameters required by optimization, including day-ahead prediction data of wind energy, light energy and stored energy, are obtained;
s2: establishing a master-slave game model, taking a network group control center as a leader, setting an initial internal price by the leader, taking each ELN sub-network as a follower, making a decision according to the initial internal price, and calculating a corresponding optimal strategy;
s3: the network group control center integrates the strategy set of each ELN sub-network, and calculates the internal price again by taking the benefit maximization of the network group control center as a target, and the internal price is defined as the updated internal price;
s4: each ELN subnet carries out decision making according to the updated internal price, and an optimal strategy corresponding to the updated internal price is calculated;
s5: when the game reaches Stackelberg equilibrium SE and the internal price is not updated any more, outputting a final optimization set as a day-ahead optimization result of the energy local area network group;
s6: if the game does not reach the Stackelberg balance, returning to the step S2 to optimize again according to the updated state information;
in step S1, the system model includes the following components:
s1-1, basic load model: the ELN contains three types of loads, i.e., heat load, cold load, electrical load, and the model is as follows:
heat load: provided by a gas boiler, a heat exchanger and a heat collector:
Figure FDA0002534474060000011
wherein the content of the first and second substances,
Figure FDA0002534474060000012
is the thermal power of the gas boiler in the ELNi;
Figure FDA0002534474060000013
is the heat power output by the heat exchanger;
Figure FDA0002534474060000014
is the total power of the thermal load in the ELNi;
the thermal power output by the gas boiler is related to the fuel consumption and the heat production efficiency of the boiler;
Figure FDA0002534474060000015
Figure FDA0002534474060000016
wherein the content of the first and second substances,
Figure FDA0002534474060000017
η is the maximum thermal power of the gas boiler in ELNiGBIs the heat production efficiency of the gas boiler; vGB,iThe gas consumption of the boiler in a period of time; l isNGIs the heat value of natural gas and is 9.7kWh/m3；
The total gas consumption V of the system can be obtained according to the fuel consumption of the gas turbine and the gas boilerSUMComprises the following steps:
Figure FDA0002534474060000021
wherein, VGT,iIs the natural gas consumption of the ELNi gas turbine during the period t;
the gas turbine is used as a main controllable energy supply device in the ELN system, not only provides electric energy for the ELN, but also recovers heat carried by high-temperature flue gas generated by the gas turbine by a heat recovery device, supplies heat for a heat load and supplies cold for a cold load through a heat exchanger and an absorption refrigeration device, and the relationship among the output, the heat and the gas consumption of the ELNi gas turbine is as follows:
Figure FDA0002534474060000022
Figure FDA0002534474060000023
Figure FDA0002534474060000024
wherein the content of the first and second substances,
Figure FDA0002534474060000025
generating power of the ELNi gas turbine for a period t;
Figure FDA0002534474060000026
the maximum power generation power of the ELNi gas turbine;
Figure FDA0002534474060000027
η waste heat recovery power of ELNi gas turbine in t periodcAnd ηrThe generating efficiency and the waste heat recovery efficiency of the ELNi gas turbine are achieved;
Figure FDA0002534474060000028
is the natural gas consumption of the ELNi gas turbine during the period t;
further, the power generation efficiency and the heat recovery efficiency of the gas turbine are greatly affected by the unit load factor, and the relationship between these two and the unit load factor β is as follows:
Figure FDA0002534474060000029
Figure FDA00025344740600000210
0.25≤β≤1 (10)
in the formula (I), the compound is shown in the specification,
Figure FDA00025344740600000211
the rated power generation efficiency of the gas turbine;
Figure FDA00025344740600000212
β is the unit load factor;
the heat exchanger exchanges a part for heating in the waste heat recovered from the gas turbine with water to obtain output thermal power;
Figure FDA0002534474060000031
in the formula (I), the compound is shown in the specification,
Figure FDA0002534474060000032
is that
Figure FDA0002534474060000033
A portion assigned for heating ηHXThe heat exchange efficiency of the heat exchanger;
the cold load is provided by the absorption refrigeration device and the electric refrigerator:
Figure FDA0002534474060000034
wherein the content of the first and second substances,
Figure FDA0002534474060000035
is the total power of the cold load in the ELNi;
Figure FDA0002534474060000036
the cold power output by the absorption refrigeration device is the cold power output by the absorption refrigeration device;
Figure FDA0002534474060000037
the refrigeration power of the electric refrigerator is ELNi;
the absorption refrigeration device provides a part of the waste heat for refrigeration to a heat exchanger in the device, so that the device converts cold energy;
Figure FDA0002534474060000038
in the formula (I), the compound is shown in the specification,
Figure FDA0002534474060000039
is that
Figure FDA00025344740600000310
Middle portion allocated for cooling ηACThe refrigeration efficiency of the absorption refrigeration device;
the electric refrigerator is a special load for generating a cooling load, and can be adjusted, and belongs to a determined party under the condition that the cooling load is known, and the output of the electric refrigerator of the ith ELN meets the following requirements:
Figure FDA00025344740600000311
Figure FDA00025344740600000312
wherein the content of the first and second substances,
Figure FDA00025344740600000313
input power of electric refrigerator being ELNi ηECThe refrigeration efficiency of the electric refrigerator;
Figure FDA00025344740600000314
maximum input power of the electric refrigerator of ELNi;
electrical loading: the electric load is mainly divided into two types of basic load and electric refrigerator according to whether the electric load is related to other two types, and the basic load model is as follows:
for ELNi ∈ I, the base load is as follows:
Figure FDA00025344740600000315
wherein the content of the first and second substances,
Figure FDA0002534474060000041
the method is the basic load of ELNi under the condition of adopting the electricity price of an external power grid; lambda [ alpha ]bRepresents the price of electrical energy purchased from an external power grid; lambda [ alpha ]sRepresenting the price of electricity sold to an external grid, rbRepresenting the internal purchase price, r, of the ELN groupsRepresenting the internal electricity selling price of the ELN group, the electricity price should satisfy the following constraint:
λs≤rs＜rb≤λb(17)
s1-2. energy storage system model
The energy storage system reduces the net load of a single ELN and the whole ELN group through two controllable operations of charging and discharging, the SoC of each time period is related to the charging and discharging state and the charging and discharging amount of the previous time period, and in the t time period, the working model of the energy storage system of the ith ELN is as follows:
Figure FDA0002534474060000042
Figure FDA0002534474060000043
in the formula (I), the compound is shown in the specification,
Figure FDA0002534474060000044
the energy stored by the ELNi energy storage system is t time period;
Figure FDA0002534474060000045
surplus capacity of time interval ELNi energy storage; qBES,iThe total capacity of the ELNi energy storage system;
Figure FDA0002534474060000046
the charging power of the ELNi energy storage system is t time period;
Figure FDA0002534474060000047
η discharge power of ELNi energy storage system in t periodchAnd ηdchThe charging efficiency and the discharging efficiency of the energy storage system are obtained;
moreover, the energy storage system in the ELN still needs to constrain the charging and discharging power of itself and the state of SoC, and simultaneously satisfies the requirement that the SoC state is not changed before and after operating one day:
Figure FDA0002534474060000048
Figure FDA0002534474060000049
Figure FDA00025344740600000410
in the formula (I), the compound is shown in the specification,
Figure FDA00025344740600000411
and
Figure FDA00025344740600000412
respectively representing the charge and discharge power of the ELNi energy storage system;
Figure FDA00025344740600000413
and
Figure FDA00025344740600000414
the maximum charge-discharge power of the ELNi energy storage system;
Figure FDA0002534474060000051
and
Figure FDA0002534474060000052
representing the charge-discharge state of the ELNi energy storage system, and taking 0 or 1, wherein 0 represents that the ELNi energy storage system is not in the charge-discharge state, and 1 represents that the ELNi energy storage system is in the charge-discharge state;
Figure FDA0002534474060000053
representing the charge and discharge power of the ELNi energy storage system in a t period;
Figure FDA0002534474060000054
the residual capacity of the ELNi energy storage system is the upper and lower limits;
s1-3 renewable energy model
The photovoltaic system has a maximum power point tracking function, can be adjusted according to the illumination intensity and the ambient temperature, tracks and outputs the maximum power in the time period, and the output of the photovoltaic system of the ELNi is as follows:
Figure FDA0002534474060000055
in the formula, PPV,iAverage value of all photovoltaic system power;
the fan system converts the mechanical energy of wind into electric energy, the output power fluctuates along with the change of the local average wind speed in the time period, and the output of the ELNi fan system is as follows:
Figure FDA0002534474060000056
in the formula, PWT,iThe average value of all fan system power is obtained;
s1-4.ELN Power balance model
Obtaining an electric power balance model of an ith ELN through various components of the ELN on a power supply side and a demand side:
Figure FDA0002534474060000057
Figure FDA0002534474060000058
wherein the content of the first and second substances,
Figure FDA0002534474060000059
the exchange power of ELNi and the network group;
Figure FDA00025344740600000510
is a transmission line power constraint.
2. The energy internet optimization control method based on the master-slave game as claimed in claim 1, wherein the master-slave game model in step S2 is established as follows:
s2-1. yield model of Single ELN
The role of power balancing is to reduce the net load of the ELN, where power balancing is set to exchange power with the ELN link
Figure FDA00025344740600000511
The profit of the electric energy transaction is the profit of the transaction within the grid, the electricity purchase price and the electricity sale price between the grid control center and each ELN sub-network are used, and the profit is the profit when the exchange power on the ELN connection is positive
Figure FDA0002534474060000061
Otherwise, the benefit is
Figure FDA0002534474060000062
The benefit of gas consumption is expressed as the inverse of the gas consumption cost;
thus, the utility function for ELN is obtained as:
Figure FDA0002534474060000063
where ρ is workCorrelation coefficient of rate balancing effect, order of magnitude and
Figure FDA0002534474060000064
the same; r isNGIs the gas price;
s2-2. revenue model of ELN cluster control center
Since the ELN in the system performs power interaction through one bus and performs energy interaction with an external power grid through the bus, certain constraint is provided for the power of the bus;
Figure FDA0002534474060000065
Figure FDA0002534474060000069
the network group control center is a mechanism manually set in the system, is used as a leader of a master-slave game model, and has the optimization goal of maximizing the benefits of the network group control center, and the revenue function of the network group control center set for the purpose is as follows:
Figure FDA0002534474060000066
wherein the content of the first and second substances,
Figure FDA0002534474060000067
and
Figure FDA0002534474060000068
the sum of power is exchanged for all ELNs purchasing and selling electricity during the time period t.
3. The energy internet optimization control method based on the master-slave game as claimed in claim 2, wherein in step S3, the master-slave game is implemented as follows:
the network group control center provides an initial internal price, and simulates each ELN to make a sound to the initial internal priceAnd obtaining the determined electric load size, and carrying out local optimization according to the utility function of each ELN to obtain all optimal strategies of each ELN, thereby obtaining the interactive power u of each ELN and the network group control centergrid(ii) a The network group control center optimizes the optimization with the maximization of the benefit as the target according to the reaction of each ELN to the initial internal electricity price and the influence of the internal electricity price on the electric loadbAnd rsThe network group control center uses the value as the initial value of the next optimization to carry out iteration, and finally the optimal r is obtainedbAnd rsAnd obtaining a scheduling scheme of each ELN according to the scheduling scheme;
the game model thus formed is as follows:
L＝{(I∪{GM}),{PLoad,i}i∈I,{Rb},{Rs},{Ui}i∈I,R} (31)
the composition comprises the following components:
1) the set I of the ELNs is a follower, and a response network group control center GM is used as an internal interaction electricity price set by the leader;
2)PLoad,iis a strategy set of ELNi adjusting load and the corresponding variable PLoad,iContaining constraints
Figure FDA0002534474060000071
3)Rb、RsIs a policy set of GM, corresponding to decision variable r of GMbAnd rs；
4)UiFor the yield function of ELNi, the upper layer optimization can be performed by the variable PLoad,iAnd constraints (5) - (11), (14) - (15), (17) - (21);
5) and R is a yield function of the GM, and the effect is to obtain the profit of energy interaction in the ELN group and trade with an external power grid.
4. The energy internet optimization control method based on the master-slave game as claimed in claim 3, wherein in step S5, the Stackelberg balancing is implemented as follows:
based on the master-slave game model in the step S3, the cluster control center determines the optimal price through the optimal reaction of each ELN, each ELN determines the optimal value of its own decision on the basis of the price, and the mode of achieving the above scheme through the master-slave game is the Stackelberg balance;
in the Stackelberg game L defined in (31), when a policy set is in use
Figure FDA0002534474060000072
Is composed of
Figure FDA0002534474060000073
Wherein, for
Figure FDA0002534474060000074
Are all provided with
Figure FDA0002534474060000075
Namely, it is
Figure FDA0002534474060000076
Is composed of
Figure FDA0002534474060000077
A set of (a); while
Figure FDA0002534474060000078
Therefore, when the above condition is satisfied, namely the game reaches SE, the SE electricity price
Figure FDA0002534474060000079
And
Figure FDA00025344740600000710
and load demand at SE
Figure FDA00025344740600000711
And (4) uniquely determining.
5. The energy Internet optimization control method based on the master-slave game as claimed in any one of claims 1 to 4, wherein in step S6, it is determined whether the updated internal price of electricity maximizes the profit of the network group control center, if the updated internal price is not changed any more, the final optimization strategy set is output as the optimization result of the energy local area network group before the day, otherwise, the step S2 is skipped to perform the optimization again.
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