CN114781896A - Low-carbon scheduling method and system for multi-energy hub comprehensive energy system - Google Patents
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Abstract
The invention belongs to the technical field of comprehensive energy systems, and provides a low-carbon scheduling method and system for a comprehensive energy system of a multi-energy concentrator. Constructing a target function according to the electricity purchasing cost, the gas purchasing cost and the carbon transaction cost of the multi-energy hub comprehensive energy system, and constructing a multi-main-body cooperation game model by combining equipment output constraint, energy balance constraint and tie line transmission power constraint; acquiring load, energy price, carbon transaction price and equipment parameters; based on the load, the energy price, the carbon transaction price and the equipment parameters, a multi-principal cooperation game model is adopted to solve and obtain an optimized scheduling strategy, and the income of each principal when not in alliance, the alliance income when partial alliances and the total income when complete alliances are realized; and processing the income of each main body when not in alliance, the alliance income when partial alliance and the total income when complete alliance by adopting different distribution strategies to obtain the income of each main body in the comprehensive energy system and verify the stability of different distribution strategies.
Description
Technical Field
The invention belongs to the technical field of comprehensive energy systems, and particularly relates to a low-carbon scheduling method and system for a comprehensive energy system of a multi-energy hub.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
With the development of distributed energy technology, a plurality of Energy Hubs (EH) are generally connected to the same energy distribution network in an Integrated Energy System (IES), and each energy hub fully utilizes the natural characteristics and demand characteristics of itself under the unified scheduling of the central energy controller in the area of the IES, so as to realize coordinated operation and complementation and improve energy supply reliability. However, the multi-energy hub IES has the characteristics of multiple subjects and strong coupling, and not only the interaction of multiple heterogeneous energy flows inside each energy hub but also the energy flow interaction among the energy hubs need to be considered, so that it is more difficult to perform optimal scheduling on the energy hubs. Moreover, most of the current research only considers the role of interconnection of the multi-energy hub in reducing the system operation cost, and lacks guidance of a proper carbon reduction mechanism, so that the potential of interconnection of the multi-energy hub in reducing carbon emission cannot be fully exploited, and the problem of benefit distribution among a plurality of subjects is rarely involved.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides a low-carbon scheduling method and system for a multi-energy hub comprehensive energy system, which introduces a carbon transaction mechanism in the aspect of low-carbon technology, and adds carbon emission into a target function in the form of carbon transaction cost; in the aspect of multi-subject benefit distribution, a multi-subject cooperative game model is established for extra benefits generated by alliance cooperation, benefits are distributed for the second time by adopting a Shapley value method, a kernel method, a DP method and the like, and the stability of each distribution strategy is verified.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a low-carbon dispatching method for a multi-energy hub comprehensive energy system.
A low-carbon scheduling method for a multi-energy hub comprehensive energy system comprises the following steps:
constructing an objective function according to the electricity purchasing cost, the gas purchasing cost and the carbon transaction cost of the multi-energy hub comprehensive energy system, and constructing a multi-main-body cooperation game model by combining an equipment output constraint, an energy balance constraint and a tie line transmission power constraint;
acquiring load, energy price, carbon transaction price and equipment parameters;
based on the load, the energy price, the carbon transaction price and the equipment parameters, a multi-principal cooperation game model is adopted to solve and obtain an optimized scheduling strategy, and the income of each principal when not in alliance, the alliance income when partial alliances and the total income when complete alliances are realized;
processing the income of each main body when not in alliance, the alliance income when in part of alliances and the total income when in complete alliances by adopting different distribution strategies to obtain the income of each main body in the comprehensive energy system;
and the DP index is adopted to verify the stability of different distribution strategies, and the optimal distribution strategy is selected to take the benefits of each main body in the system into consideration, so that the running cost and the carbon emission of the system are reduced.
The invention provides a low-carbon dispatching system of a multi-energy hub comprehensive energy system.
A low-carbon scheduling system of a multi-energy hub comprehensive energy system comprises:
a model building module configured to: constructing an objective function according to the electricity purchasing cost, the gas purchasing cost and the carbon transaction cost of the multi-energy hub comprehensive energy system, and constructing a multi-subject cooperative game model by combining equipment output constraint, energy balance constraint and tie line transmission power constraint;
a data acquisition module configured to: acquiring load, energy price, carbon transaction price and equipment parameters;
a revenue calculation module configured to: based on the load, the energy price, the carbon transaction price and the equipment parameters, a multi-principal cooperation game model is adopted, and an optimized scheduling strategy, profits of each principal when not in alliance, alliance profits when partial alliance and total profits when complete alliance are obtained through solving;
a revenue processing module configured to: processing the income of each main body when not in alliance, the alliance income when in partial alliance and the total income when in complete alliance by adopting different distribution strategies to obtain the income of each main body in the comprehensive energy system;
a verification module configured to: and the DP indexes are adopted to verify the stability of different distribution strategies, and the optimal distribution strategy is selected to take the benefits of each main body in the system into account, so that the running cost and the carbon emission of the system are reduced.
A third aspect of the invention provides a computer-readable storage medium.
A computer readable storage medium, having stored thereon a computer program, which when executed by a processor, performs the steps of the low carbon scheduling method for multi-energy hub integrated energy system as described in the first aspect.
A fourth aspect of the invention provides a computer apparatus.
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the low carbon scheduling method of the integrated energy system of the multi-energy hub according to the first aspect.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a low-carbon scheduling method and a low-carbon scheduling system for a comprehensive energy system of a multi-energy hub, which can reduce the operation cost and the carbon emission by introducing a carbon transaction mechanism in optimized scheduling and realize the win-win of economy and environmental protection; and for the residual cooperation generated by complete alliance among different energy hubs, a cooperative game method is adopted, so that fair distribution can be realized, and the benefits of all participants are taken into consideration.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a flowchart of a low-carbon scheduling method for a multi-energy hub integrated energy system according to an embodiment of the present invention
FIG. 2 is a diagram illustrating a structure of an integrated energy system according to an embodiment of the present invention;
fig. 3 is a structural diagram of an energy hub according to an embodiment of the present invention;
fig. 4(a) is a diagram illustrating an electric power dispatching strategy of the energy hub EH1 in the integrated energy system according to an embodiment of the present invention;
fig. 4(b) is a diagram illustrating an electric power dispatching strategy of the energy hub EH2 in the integrated energy system according to an embodiment of the present invention;
fig. 4(c) is a diagram illustrating an electric power dispatching strategy of the energy hub EH3 in the integrated energy system according to an embodiment of the present invention;
fig. 5(a) is a schematic diagram illustrating a thermal energy dispatching strategy of the energy hub EH1 in the integrated energy system according to an embodiment of the present invention;
fig. 5(b) is a diagram illustrating a thermal energy scheduling strategy of the energy hub EH2 in the integrated energy system according to an embodiment of the present invention;
fig. 5(c) is a diagram illustrating a thermal energy scheduling strategy of the energy hub EH3 in the integrated energy system according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
It is noted that the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of the present disclosure. It should be noted that each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the logical function specified in the various embodiments. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Example one
As shown in fig. 1, the embodiment provides a low-carbon scheduling method for a multi-energy hub integrated energy system, and the embodiment is illustrated by applying the method to a server, it is to be understood that the method may also be applied to a terminal, and may also be applied to a system including a terminal and a server, and is implemented by interaction between the terminal and the server. The server can be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and can also be a cloud server for providing basic cloud computing services such as cloud service, a cloud database, cloud computing, cloud functions, cloud storage, a network server, cloud communication, middleware service, domain name service, security service CDN (content delivery network), a big data and artificial intelligence platform and the like. The terminal may be, but is not limited to, a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and the like. The terminal and the server may be directly or indirectly connected through wired or wireless communication, and the application is not limited herein. In this embodiment, the method includes the steps of:
constructing an objective function according to the electricity purchasing cost, the gas purchasing cost and the carbon transaction cost of the multi-energy hub comprehensive energy system, and constructing a multi-main-body cooperation game model by combining an equipment output constraint, an energy balance constraint and a tie line transmission power constraint;
acquiring load, energy price, carbon transaction price and equipment parameters;
based on the load, the energy price, the carbon transaction price and the equipment parameters, a multi-principal cooperation game model is adopted to solve and obtain an optimized scheduling strategy, and the income of each principal when not in alliance, the alliance income when partial alliances and the total income when complete alliances are realized;
processing the income of each main body when not in alliance, the alliance income when in part of alliances and the total income when in complete alliances by adopting different distribution strategies to obtain the income of each main body in the comprehensive energy system;
and the DP indexes are adopted to verify the stability of different distribution strategies, and the optimal distribution strategy is selected to take the benefits of each main body in the system into account, so that the running cost and the carbon emission of the system are reduced.
The specific technical solution of the embodiment can be implemented by referring to the following contents;
the comprehensive energy system consists of three energy hubs, and the three energy hubs can mutually exchange electric power and thermal power, as shown in fig. 2; the energy conversion device comprises a fan, a gas turbine, a gas boiler, an electric refrigerator and an absorption refrigerator, and the energy storage device comprises a storage battery and a heat storage device. The electric load is satisfied by the electricity purchased by a superior power grid, a gas turbine and a storage battery; the heat load is satisfied by the gas turbine, the gas boiler and the heat storage device; the cooling load is satisfied by the electric refrigerator and the absorption refrigerator. As shown in fig. 3. The optimized scheduling method reasonably arranges the output of each unit so as to reduce the running cost and carbon emission, and then fairly distributes the benefits of each participant through cooperative game.
In the embodiment, a multi-principal cooperative game model of the system is established, the optimized scheduling strategy of each alliance benefit and the system is obtained through the solution of a Cplex solver, the benefits are secondarily distributed by adopting a Shapley value method, a kernel method, a DP method and the like, the stability of each distribution strategy is verified through a DP index, and a proper distribution strategy is selected.
The objective functions and constraints of the multi-subject cooperative game model are analyzed as follows:
1. objective function
The objective function of the model proposed in this embodiment considers both the economy and the environmental protection of the system, where the environmental protection objective is expressed by the carbon trading cost, and therefore the objective function can be expressed as:
in the formula, FEHSThe total operation cost of the multi-energy concentrator system, N is the number of the energy concentrators, Ce,iFor the electricity purchase cost of the energy concentrator i, Cg,iIn order to reduce the gas purchase cost of the energy concentrator i,
is the carbon trading cost of the energy hub i.
The electricity purchase cost and the gas purchase cost of the energy hub i are respectively proportional to the electricity purchase power and the gas purchase power, and therefore can be expressed as follows:
in the formula (I), the compound is shown in the specification,
for the purchase of electrical power from the distribution grid for the energy hub i,
for the gas purchasing power of the energy hub i,
for the electricity price of purchasing electricity from the distribution network at the time t,
is the natural gas price at time t.
The carbon trading cost of an energy hub i, in relation to its carbon emissions and carbon emission quota, can be expressed as:
wherein m is the type of the carbon emission source in the energy hub i and mainly comprises outsourcing electric power, a gas turbine and a gas boiler,
and
the carbon emission quota and the carbon emission amount of the mth carbon emission source in the energy hub i at the time t are respectively as follows:
the outsourcing power is regarded as thermal power, and the carbon emission quota can be expressed as:
De＝λePe
in the formula DeCarbon quota, lambda, allocated for outsourcing powereCarbon quota coefficient, P, per unit of electricity producedeThe electric quantity is purchased externally.
The gas turbine generates electricity while additionally providing heat energy, so its carbon quota can be expressed as:
Dgt＝λh(cehPgt+Hgt)
in the formula DgtThe carbon quota allocated for the gas turbine,λhcarbon quota coefficient per heat production, cehConversion coefficient, P, for converting electric quantity into heatgtAnd HgtRespectively the electricity and heat production of the gas turbine.
The gas boiler is used only for providing thermal energy, and therefore its carbon quota can be expressed as:
Dgb＝λhHgb
in the formula, DgbCarbon quota assigned to gas boiler, HgbIs the heat production of the gas boiler.
The actual carbon emissions of the outsourcing power, gas turbine, gas boiler can be represented as a model proportional to the amount of electricity and heat, respectively:
Ae＝εePe
Agt＝εh(cehPgt+Hgt)
Agb＝εhHgb
in the formula, Ae、AgtAnd AgbRespectively, carbon emissions, epsilon, from outsourcing electric power, gas turbines and gas boilerseCarbon emission coefficient of coal-fired power plant for generating unit power electricity; after the electric quantity is converted into heat, the equivalent heat supply quantity of the gas turbine and the gas boiler is close.
2. Constraint conditions
(1) And (3) equipment output constraint:
(a) gas turbine
In the formula (I), the compound is shown in the specification,
and
respectively the power generated by the gas turbine and the power generated by the heat generation at the time t,
and
minimum and maximum technical output for the gas turbine to generate electricity, respectively;
and
respectively the minimum and maximum technical contribution to the heat production of the gas turbine.
(b) Gas boiler
In the formula (I), the compound is shown in the specification,
the heat production power of the gas boiler at the moment t;
and
maximum and minimum heat-generating power, respectively.
(c) Electric refrigerator
In the formula (I), the compound is shown in the specification,
the refrigeration power of the electric refrigerator at the moment t;
and
respectively the minimum and maximum refrigeration power of the electric refrigerator.
(d) Absorption refrigerator
In the formula (I), the compound is shown in the specification,
in order to be the refrigeration coefficient of an absorption chiller,
and
respectively, the minimum and maximum refrigeration power of the absorption chiller.
(e) Energy storage device
In the formula, the subscript x is the kind of energy, and the embodiment is electric energy and thermal energy.
For the energy stored at time t, θxIs the energy self-loss rate, etax,cAnd ηx,dThe energy storage efficiency and the energy release efficiency are respectively,
and
respectively an energy storage power and an energy release power,
and
maximum stored and released power, u, respectivelyxIs a variable of 0 to 1 introduced to ensure that energy storage and energy release cannot be performed simultaneously,
and
the energy storage device is respectively the maximum storage capacity and the minimum storage capacity, so that the scheduling management of the energy storage device is facilitated, and the last item ensures that the stored energy before and after the scheduling period is the same.
(2) Energy balance constraints (the superscript t is omitted here for simplicity of writing):
in the formula, Le,i、Lh,iAnd Lc,iRespectively the electricity, heat and cold loads of the energy concentrator i; etawt,i、
ηgb,i、COPec,iAnd COPac,iThe energy conversion efficiencies of the corresponding energy conversion devices are respectively; pe,i、Pwt,i、Ggt,i、Ggb,i、Pec,i、Hac,iThe power is purchased power and input power of the corresponding energy conversion device; p isijAnd HijElectric and thermal energy, P, respectively, supplied from an energy hub i to an energy hub jjiAnd HjiRespectively the electric energy and the heat energy which are transmitted from the energy concentrator j to the energy concentrator i.
(3) Junctor transmission power constraints
(a) Transmission power constraint with upper network
In the formula (I), the compound is shown in the specification,
and
this constraint is used to ensure that the power transmitted by the tie does not exceed its limits for the maximum power transmitted by the power and gas ties.
(b) Transmission power constraints between energy hubs
In the formula (I), the compound is shown in the specification,
for the maximum transmit power constraint of the power link between the energy hubs i and j,
and
respectively representing the direction of transfer of electrical energy between the energy hubs i and j,
a value of 1 indicates that power is being transferred from i to j,
a value of 1 indicates that power is transferred from j to i.
In the formula (I), the compound is shown in the specification,
for the maximum transfer power constraint of the thermal energy link between the energy hubs i and j,
and
respectively representing the direction of transfer of thermal energy between the energy hubs i and j,
a value of 1 indicates that heat energy is transferred from i to j,
a value of 1 indicates that heat is transferred from j to i.
And at this moment, a multi-main-body cooperation game model of the comprehensive energy system of the multi-energy hub is established, and the CPLEX solver is adopted to solve the 24-hour output of each unit in each energy hub, so that the optimized dispatching of the comprehensive energy system is realized. In order to verify the advantages of introducing carbon transaction and considering power interaction in the multi-energy hub IES, the following four scenes are specially set:
scene one: and the three energy hubs operate independently without considering a carbon transaction mechanism, and no interaction of electric energy and heat energy exists among the energy hubs.
Scene two: considering the carbon transaction mechanism, the three energy hubs operate independently, and no interaction of electric energy and heat energy exists among the energy hubs.
Scene three: and the three energy hubs operate cooperatively without considering a carbon transaction mechanism, and electric power and thermal power are interacted through an electric energy and thermal energy connecting line.
Scene four: considering a carbon transaction mechanism, the three energy hubs operate cooperatively, and electric power and heat power are interacted through an electric energy and heat energy connecting line.
The optimized scheduling results in different scenarios are shown in table 1.
TABLE 1 optimized scheduling results under different scenarios
(1) Analysis of influence of power interaction among energy concentrators on scheduling result
Comparing the first, second and fourth scenes, it can be seen that when the energy hubs operate cooperatively, i.e. power can be exchanged through the tie lines, the operation cost is reduced by 1089 yuan and 1078 yuan, respectively, compared with the independent operation, because when in the independent operation mode, the energy supply mainly depends on the respective supply capacity, and in the cooperative mode, energy sharing is realized through the power exchange between the energy hubs, as shown in the inter-energy hub transmission power of fig. 4(a) -4 (c) and 5(a) -5 (c), the energy use is more reasonable, thereby reducing the operation cost of the whole system. Comparing with the first scenario and the third scenario, when power interaction is taken into consideration, the carbon emission is increased instead, which is caused by the increase of the electricity purchasing quantity; compared with the second scene and the fourth scene, the carbon emission is obviously reduced, which also fully explains the necessity of introducing a carbon trading mechanism.
(2) Analysis of whether to consider the influence of carbon transactions on scheduling results
Compared with the first scene, the second scene and the third scene, when a carbon trading mechanism is considered, the running cost is respectively reduced by 2139 yuan and 2127 yuan, the carbon emission is respectively reduced by 5.3 tons and 7 tons, and the win-win effect of economy and environmental protection is realized. As can be seen from table 1, this is because when considering carbon trading, the system will tend to use cleaner gas turbines to supply power, so the amount of electricity purchased from the upper grid is reduced, and the amount of gas purchased is increased, so the amount of carbon emissions is reduced. As can be seen from fig. 4(a) -4 (c), after the carbon trading mechanism is introduced, the electric energy is mainly satisfied by the fan and the gas turbine, and the power is purchased less from the upper-level power grid, so that the carbon emission of the whole system is less than the carbon quota, the system can sell the carbon quota to obtain the profit, and the carbon trading costs corresponding to the scenarios two and four in table 1 are negative values, so the operating cost of the system is reduced compared with the operating cost without considering the carbon trading.
Taking a scenario four as an example, the scheduling policy of the IES is specifically analyzed.
Fig. 4(a) -4 (c) are power scheduling strategies for a multi-energy hub IES considering carbon transactions in a cooperative mode, with power balancing at any time during the scheduling period. Fig. 4(a), 4(b), and 4(c) are the results of the electric energy optimized operation of the energy hubs 1, 2, and 3, respectively, and thus it can be seen that the electric load of each energy hub is mainly satisfied by the wind turbine and the gas turbine, and the shortage is satisfied by the supply of other energy hubs and the power grid purchase, wherein the time period of power purchase from the upper power grid of each energy hub is concentrated at 23 and 24, because the electric load demand is large and the electricity price is in the valley period. Taking the energy hub 1 as an example, the storage battery is 5: 00-7: 00 is low and the power supply is surplus, and when 8: 00-9: 00. 14: the electricity is discharged when the electricity price of 00 is high and the electricity is in shortage, so that the transfer of the electricity in time is realized. Further, 3: 00 to 9: 00, the electrical load demand of the energy hub 1 is high, so that other energy hubs in the system supply electrical energy to it, in the range of 10: 00-11: 00, the electrical load demand of the energy hub 1 is low, and the generated excess electrical energy can be transmitted to other energy hubs in the system through the power grid, and for other time periods and other energy hubs in the system, the above analysis is similar, and details are not repeated here. Therefore, the mutual transmission of the electric energy among the main bodies in the system realizes the complementary mutual compensation of the electric energy, thereby improving the income.
Fig. 5(a) -5 (c) are thermal energy scheduling strategies for a multi-energy hub IES in a collaborative mode considering carbon trading, with thermal energy kept balanced at any time during the scheduling period. Fig. 5(a), 5(b), and 5(c) show the thermal energy optimization operation results of the energy hubs 1, 2, and 3, respectively, so that it can be seen that the thermal load of each energy hub is mainly satisfied by the gas turbine, and the deficiency is satisfied by the thermal energy delivered by the gas boiler and other energy hubs in the system. As can be seen from fig. 5(a), in 3: 00 and 5: 00 gas turbines have low capacity and generate insufficient heat to meet the heat load, thus starting the gas boiler to produce heat. At 19: 00. 21: 00-22: 00, turbine output is higher than thermal load demand in the energy hub 1, with the thermal storage device in the heat absorption state, and in 9: 00. 12: 00-14: 00. 20: 00, the output of the gas turbine is not enough to meet the heat load, the heat is released by the heat storage device, and the heat storage device achieves the energy buffering effect in the system. Further, in 1: 00-2: 00 gas turbine output is high and exceeds the requirement of heat load, therefore the residual heat energy can be transmitted to other energy hubs in the system through the heat supply network, so as to realize complementary utilization of energy, and other time periods and energy hubs 2 and 3 have the same analysis as the above. Because the mutual transmission of the cold energy among the energy hubs is not considered, the cold energy balance of the system is not analyzed independently.
Benefit distribution policy analysis
Cooperative gaming can be used to solve the problem of benefit distribution, in the above model, each energy hub can be considered as a participant in the game problem, and there can be seven combinations between the three energy hubs: { EH1}, { EH2}, { EH3}, { EH1, EH2}, { EH1, EH3}, { EH2, EH3}, { EH1, EH2 and EH3}, wherein each combination is regarded as an alliance, and the seven combinations can be divided into three operation modes of no alliance, partial alliance and full alliance to respectively calculate the operation cost and the benefit (reduced cost) of the seven combinations, as shown in Table 2.
TABLE 2 cost savings in different operating modes
As can be seen from table 2, the cost in the operation mode corresponding to all the partial unions and the full unions is reduced compared to that in the non-unions, and when the full unions are performed among the three energy hubs, the profit is the highest, and then the cost is reduced by 1078 yuan. From the above results, it can be calculated that the benefits of each participant under different distribution strategies are shown in table 3:
TABLE 3 benefits received by participants under different distribution strategies
To verify the stability of the three allocation schemes, DP indices under different allocation strategies were calculated, as shown in table 4:
TABLE 4 DP indices under different allocation strategies
In the sharley value allocation strategy, the DP value of EH2 is negative, and as can be seen from the above analysis, while the overall rationality and the individual rationality are satisfied, the alliance rationality cannot be satisfied, so that the alliance is unstable, and EH1 and EH3 tend not to accept the allocation strategy to form a new alliance { EH1, EH3} to obtain greater benefit. In the allocation strategy of the kernel method, although the DP value of EH3 can be the minimum value, which means that the satisfaction degree of EH3 is the highest at this time, the DP value of EH2 is close to 1 at this time, that is, the loss caused by the rejection of the allocation by EH2 to other participants is almost equal to the loss caused by itself, and EH2 may accept the allocation strategy and may threaten other participants by rejecting cooperation to obtain higher profit, and thus is also unstable. In the distribution strategy of the equal DP, the DP value of each participant is a positive number which is greater than 0 and less than 1, that is, under the distribution strategy, each participant cannot cause more loss to other participants by refusing to participate in cooperation, and the satisfaction degree to the distribution strategy is the same at this time, so that the distribution strategy is stable.
The embodiment first determines the carbon emission sources in the system, including outsourcing power, a gas turbine and a gas boiler, wherein the outsourcing power is regarded as power generated by a coal-fired unit. Then, the carbon quota is allocated to the machine set participating in the carbon trading without compensation, and if the carbon emission of the machine set exceeds the corresponding carbon quota, the carbon emission right needs to be purchased from a carbon trading market; if the carbon emissions of the unit are below the corresponding carbon quota, the corresponding carbon emissions may be sold to a carbon trading market for revenue. Therefore, the carbon emission of the system is reduced by adopting a carbon trading mode, and the low-carbon operation of the IES is promoted.
Example two
The embodiment provides a low-carbon scheduling system of a multi-energy hub comprehensive energy system.
A low-carbon scheduling system of a multi-energy hub comprehensive energy system comprises:
a model building module configured to: constructing an objective function according to the electricity purchasing cost, the gas purchasing cost and the carbon transaction cost of the multi-energy hub comprehensive energy system, and constructing a multi-subject cooperative game model by combining equipment output constraint, energy balance constraint and tie line transmission power constraint;
a data acquisition module configured to: acquiring load, energy price, carbon transaction price and equipment parameters;
a revenue calculation module configured to: based on the load, the energy price, the carbon transaction price and the equipment parameters, a multi-principal cooperation game model is adopted, and an optimized scheduling strategy, profits of each principal when not in alliance, alliance profits when partial alliance and total profits when complete alliance are obtained through solving;
a revenue processing module configured to: processing the income of each main body when not in alliance, the alliance income when in partial alliance and the total income when in complete alliance by adopting different distribution strategies to obtain the income of each main body in the comprehensive energy system;
a verification module configured to: and the DP index is adopted to verify the stability of different distribution strategies, and the optimal distribution strategy is selected to take the benefits of each main body in the system into consideration, so that the running cost and the carbon emission of the system are reduced.
It should be noted here that the model building module, the data obtaining module, the profit calculating module, the profit processing module and the verifying module are the same as the example and the application scenario realized by the steps in the first embodiment, but are not limited to the disclosure of the first embodiment. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.
EXAMPLE III
The embodiment provides a computer-readable storage medium, on which a computer program is stored, where the program, when executed by a processor, implements the steps in the low-carbon scheduling method for multi-energy hub integrated energy system as described in the first embodiment.
Example four
The embodiment provides computer equipment, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to realize the steps of the low-carbon scheduling method for the multi-energy hub integrated energy system according to the first embodiment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention 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, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by a computer program, which may be stored in a computer readable storage medium and executed by a computer to implement the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A low-carbon scheduling method for a multi-energy hub comprehensive energy system is characterized by comprising the following steps:
constructing an objective function according to the electricity purchasing cost, the gas purchasing cost and the carbon transaction cost of the multi-energy hub comprehensive energy system, and constructing a multi-main-body cooperation game model by combining an equipment output constraint, an energy balance constraint and a tie line transmission power constraint;
acquiring load, energy price, carbon transaction price and equipment parameters;
based on the load, the energy price, the carbon transaction price and the equipment parameters, a multi-principal cooperation game model is adopted, and an optimized scheduling strategy, profits of each principal when not in alliance, alliance profits when partial alliance and total profits when complete alliance are obtained through solving;
processing the income of each main body when not in alliance, the alliance income when in part of alliances and the total income when in complete alliances by adopting different distribution strategies to obtain the income of each main body in the comprehensive energy system;
and the DP indexes are adopted to verify the stability of different distribution strategies, and the optimal distribution strategy is selected to take the benefits of each main body in the system into account, so that the running cost and the carbon emission of the system are reduced.
2. The low-carbon scheduling method for the multi-energy hub integrated energy system according to claim 1, wherein the objective function is:
in the formula, FEHSThe total operation cost of the comprehensive energy system of the multi-energy concentrator, N is the number of the energy concentrators, Ce,iFor the electricity purchase cost of the energy concentrator i, Cg,iIn order to reduce the gas purchase cost of the energy concentrator i,
is the carbon trading cost of the energy hub i.
3. The low carbon scheduling method of the multi-energy hub integrated energy system of claim 1, wherein the device output constraints comprise: the power generation power constraint and the heat production power constraint of the gas turbine, the heat production power constraint of the gas boiler, the refrigeration power constraint of the electric refrigerator, the refrigeration power constraint of the absorption refrigerator and the energy storage device constraint; wherein the energy storage device constraints include stored energy, stored energy power constraints, released energy power constraints, and storage capacity constraints of the energy storage device.
4. The low carbon scheduling method of the multi-energy hub integrated energy system according to claim 1, wherein the energy balance constraint comprises: the system comprises an electric energy balance constraint, a heat energy balance constraint and a cold energy balance constraint, wherein the energy balance constraint is described by adopting an energy hub matrix model.
5. The low carbon scheduling method of the multi-energy hub integrated energy system of claim 1, wherein the tie line transmission power constraint comprises: and transmitting power constraint between the upper network and the energy hub.
6. The low-carbon scheduling method for the multi-energy hub integrated energy system according to claim 1, wherein the earnings of each subject during non-alliance, the alliance earnings during partial alliance and the total earnings during full alliance are respectively the earnings of each energy hub during independent operation, the alliance earnings during cooperative operation of partial energy hubs and the total earnings during full cooperative operation of all energy hubs.
7. The low-carbon scheduling method for the multi-energy hub integrated energy system according to claim 1, wherein the allocation strategy comprises a Shapley value method, a kernel method and an equal DP method.
8. The utility model provides a multi-energy concentrator integrated energy system low carbon dispatch system which characterized in that includes:
a model building module configured to: constructing an objective function according to the electricity purchasing cost, the gas purchasing cost and the carbon transaction cost of the multi-energy hub comprehensive energy system, and constructing a multi-subject cooperative game model by combining equipment output constraint, energy balance constraint and tie line transmission power constraint;
a data acquisition module configured to: acquiring load, energy price, carbon transaction price and equipment parameters;
a revenue calculation module configured to: based on the load, the energy price, the carbon transaction price and the equipment parameters, a multi-principal cooperation game model is adopted to solve and obtain an optimized scheduling strategy, and the income of each principal when not in alliance, the alliance income when partial alliances and the total income when complete alliances are realized;
a revenue processing module configured to: processing the income of each main body when not in alliance, the alliance income when in part of alliances and the total income when in complete alliances by adopting different distribution strategies to obtain the income of each main body in the comprehensive energy system;
a verification module configured to: and the DP indexes are adopted to verify the stability of different distribution strategies, and the optimal distribution strategy is selected to take the benefits of each main body in the system into account, so that the running cost and the carbon emission of the system are reduced.
9. A computer readable storage medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the steps of the low carbon scheduling method for the multi-energy hub integrated energy system according to any one of claims 1 to 7.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to perform the steps of the low carbon scheduling method of the multi-energy hub integrated energy system according to any one of claims 1 to 7.
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