A low-carbon scheduling method and system for a multi-energy hub integrated energy system

Abstract

The present invention belongs to the technical field of integrated energy systems, and provides a low-carbon scheduling method and system for a multi-energy hub integrated energy system. The method includes constructing an objective function according to the electricity purchase cost, gas purchase cost and carbon trading cost of the multi-energy hub integrated energy system, and constructing a multi-agent cooperative game model in combination with equipment output constraints, energy balance constraints and interconnection line transmission power constraints; obtaining load, energy price, carbon trading price and equipment parameters; based on load, energy price, carbon trading price and equipment parameters, using a multi-agent cooperative game model to solve and obtain an optimized scheduling strategy, as well as the income of each subject when there is no alliance, the alliance income when there is a partial alliance and the total income when there is a complete alliance; using different allocation strategies to process the income of each subject when there is no alliance, the alliance income when there is a partial alliance and the total income when there is a complete alliance, to obtain the income of each subject in the integrated energy system, and to verify the stability of different allocation strategies.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated energy systems, and in particular relates to a low-carbon scheduling method and system for an integrated energy system of a multi-energy hub. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the development of distributed energy technology, multiple energy hubs (EH) are usually connected to the same energy distribution network in an integrated energy system (IES). Under the unified dispatch of the regional central energy controller of IES, each energy hub makes full use of its own natural endowment and demand characteristics to achieve coordinated operation and complementarity and improve energy supply reliability. However, multi-energy hub IES has the characteristics of multiple subjects and strong coupling. It is necessary to consider not only the interaction of multiple heterogeneous energy flows within each energy hub, but also the interaction of energy flows between energy hubs. Therefore, it is more difficult to optimize its dispatch. Moreover, most of the current studies only consider the role of multi-energy hub interconnection in reducing system operating costs, lack the guidance of appropriate carbon reduction mechanisms, and thus fail to fully tap its potential in reducing carbon emissions. In addition, the issue of interest distribution among multiple subjects is rarely involved. Summary of the invention

[0004] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a low-carbon scheduling method and system for an integrated energy system of a multi-energy hub. In terms of low-carbon technology, a carbon trading mechanism is introduced, and carbon emissions are added to the objective function in the form of carbon trading costs; in terms of multi-subject benefit distribution, a multi-subject cooperative game model is established for the additional benefits generated by alliance cooperation, and the Shapley value method, the nucleolus method, and the equal DP method are used for secondary distribution of benefits, and the stability of each distribution strategy is verified.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] A first aspect of the present invention provides a low-carbon scheduling method for a multi-energy hub integrated energy system.

[0007] A low-carbon dispatching method for a multi-energy hub integrated energy system, comprising:

[0008] The objective function is constructed based on the electricity purchase cost, gas purchase cost and carbon trading cost of the multi-energy hub integrated energy system, and a multi-agent cooperative game model is constructed by combining the equipment output constraints, energy balance constraints and interconnection line transmission power constraints.

[0009] Obtain load, energy price, carbon trading price and equipment parameters;

[0010] Based on load, energy price, carbon trading price and equipment parameters, a multi-agent cooperative game model is used to solve the optimal dispatching strategy, as well as the benefits of each agent when there is no alliance, the alliance benefits when there is a partial alliance, and the total benefits when there is a complete alliance.

[0011] Different allocation strategies are used to process the benefits of each subject when there is no alliance, the alliance benefits when there is a partial alliance, and the total benefits when there is a complete alliance, and the benefits of each subject in the integrated energy system are obtained;

[0012] The DP indicator is used to verify the stability of different allocation strategies, and the optimal allocation strategy is selected to take into account the interests of each entity in the system and reduce the operating costs and carbon emissions of the system.

[0013] A second aspect of the present invention provides a low-carbon dispatching system for a multi-energy hub integrated energy system.

[0014] A multi-energy hub integrated energy system low-carbon dispatching system, comprising:

[0015] A model building module is configured to: construct an objective function according to the electricity purchase cost, gas purchase cost and carbon trading cost of the multi-energy hub integrated energy system, and construct a multi-agent cooperative game model in combination with equipment output constraints, energy balance constraints and interconnection line transmission power constraints;

[0016] A data acquisition module, which is configured to: acquire load, energy price, carbon trading price and equipment parameters;

[0017] The revenue calculation module is configured to: based on load, energy price, carbon trading price and equipment parameters, adopt a multi-agent cooperative game model to solve the optimal dispatching strategy, as well as the revenue of each agent when there is no alliance, the alliance revenue when there is a partial alliance and the total revenue when there is a full alliance;

[0018] The income processing module is configured to: use different allocation strategies to process the income of each subject when there is no alliance, the alliance income when there is a partial alliance, and the total income when there is a full alliance, so as to obtain the income of each subject in the integrated energy system;

[0019] The verification module is configured to: use the DP indicator to verify the stability of different allocation strategies, select the optimal allocation strategy to take into account the interests of each subject in the system and reduce the operating costs and carbon emissions of the system.

[0020] A third aspect of the present invention provides a computer-readable storage medium.

[0021] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the low-carbon scheduling method for a multi-energy hub integrated energy system as described in the first aspect above.

[0022] A fourth aspect of the present invention provides a computer device.

[0023] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the low-carbon scheduling method of a multi-energy hub integrated energy system as described in the first aspect above are implemented.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] The present invention provides a low-carbon scheduling method and system for an integrated energy system with multiple energy hubs. By introducing a carbon trading mechanism in the optimized scheduling, it is possible to reduce operating costs and carbon emissions, and achieve a win-win situation in terms of economy and environmental protection. For the cooperation surplus generated by a complete alliance between different energy hubs, a cooperative game method is adopted to achieve fair distribution while taking into account the interests of all participants. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0027] Figure 1 Flow chart of a low-carbon scheduling method for a multi-energy hub integrated energy system according to a first embodiment of the present invention

[0028] Figure 2 This is a structural diagram of a comprehensive energy system according to Embodiment 1 of the present invention;

[0029] Figure 3 This is a structural diagram of an energy hub according to Embodiment 1 of the present invention;

[0030] FIG4( a ) is a diagram of an electric energy dispatching strategy of an energy hub EH1 in an integrated energy system shown in the first embodiment of the present invention;

[0031] FIG4( b ) is a diagram of the electric energy dispatching strategy of the energy hub EH2 in the integrated energy system shown in the first embodiment of the present invention;

[0032] FIG4( c ) is a diagram of the electric energy dispatching strategy of the energy hub EH3 in the integrated energy system shown in the first embodiment of the present invention;

[0033] FIG5(a) is a diagram of a thermal energy scheduling strategy of an energy hub EH1 in an integrated energy system shown in the first embodiment of the present invention;

[0034] FIG5( b ) is a diagram of a thermal energy scheduling strategy for an energy hub EH2 in an integrated energy system according to the first embodiment of the present invention;

[0035] FIG5(c) is a diagram showing a thermal energy scheduling strategy of an energy hub EH3 in an integrated energy system according to the first embodiment of the present invention. DETAILED DESCRIPTION

[0036] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0037] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0038] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0039] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the methods and systems according to various embodiments of the present disclosure. It should be noted that each box in the flowchart or block diagram can represent a module, a program segment, or a part of a code, and the module, program segment, or a part of a code may include one or more executable instructions for implementing the logical functions specified in each embodiment. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the flowchart and / or block diagram, and the combination of boxes in the flowchart and / or block diagram can be implemented using a dedicated hardware-based system that performs a specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0040] Embodiment 1

[0041] like Figure 1As shown, this embodiment provides a low-carbon scheduling method for an integrated energy system of a multi-energy hub. This embodiment uses the method applied to a server as an example for illustration. It can be understood that the method can also be applied to a terminal, and can also be applied to a system including a terminal, a server, and a server, and is implemented through the interaction between the terminal and the server. The server can be an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communications, middleware services, domain name services, security services CDN, and big data and artificial intelligence platforms. The terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited to this. The terminal and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in this application. In this embodiment, the method includes the following steps:

[0042] The objective function is constructed based on the electricity purchase cost, gas purchase cost and carbon trading cost of the multi-energy hub integrated energy system, and a multi-agent cooperative game model is constructed by combining the equipment output constraints, energy balance constraints and interconnection line transmission power constraints.

[0043] Obtain load, energy price, carbon trading price and equipment parameters;

[0044] Based on load, energy price, carbon trading price and equipment parameters, a multi-agent cooperative game model is used to solve the optimal dispatching strategy, as well as the benefits of each agent when there is no alliance, the alliance benefits when there is a partial alliance, and the total benefits when there is a complete alliance.

[0045] Different allocation strategies are used to process the benefits of each subject when there is no alliance, the alliance benefits when there is a partial alliance, and the total benefits when there is a complete alliance, and the benefits of each subject in the integrated energy system are obtained;

[0046] The DP indicator is used to verify the stability of different allocation strategies, and the optimal allocation strategy is selected to take into account the interests of each entity in the system and reduce the operating costs and carbon emissions of the system.

[0047] The specific technical solution of this embodiment can be implemented by referring to the following contents:

[0048] The integrated energy system is composed of three energy hubs, which can exchange electrical and thermal power with each other. Figure 2As shown; it consists of energy conversion equipment and energy storage equipment, among which the energy conversion equipment includes fans, gas turbines, gas boilers, electric refrigerators, and absorption refrigerators, and the energy storage equipment includes batteries and heat storage devices. The electrical load is met by the purchase of electricity from the upper power grid, gas turbines, and batteries; the thermal load is met by gas turbines, gas boilers, and heat storage devices; and the cold load is met by electric refrigerators and absorption refrigerators. Figure 3 The output of each unit is reasonably arranged through the optimization scheduling method described in this embodiment, thereby reducing operating costs and carbon emissions, and then the benefits of each participant are fairly distributed through cooperative game.

[0049] In this embodiment, a multi-agent cooperative game model of the system is established, and the benefits of each alliance and the optimal scheduling strategy of the system are obtained by using the Cplex solver. The Shapley value method, the nucleolus method, and the equal DP method are used to perform secondary distribution of the benefits. The stability of each distribution strategy is verified by the DP indicator, and the appropriate distribution strategy is selected.

[0050] The objective function and constraints of the multi-agent cooperative game model are analyzed below:

[0051] 1. Objective Function

[0052] The objective function of the model proposed in this embodiment takes into account both the economy and environmental protection of the system, where the environmental protection target is represented by the carbon trading cost. Therefore, the objective function can be expressed as:

[0053]

[0054] In the formula, F EHS is the total operating cost of the multi-energy hub system, N is the number of energy hubs, C e,i is the electricity purchase cost of energy hub i, C g,i is the gas purchase cost of energy hub i, Carbon trading costs for energy hub i.

[0055] The electricity purchase cost and gas purchase cost of energy hub i are proportional to its purchased electricity power and gas purchase power, respectively, and can be expressed as:

[0056]

[0057]

[0058] In the formula, is the power purchased by energy hub i from the distribution grid, is the gas purchasing power of energy hub i, is the electricity price purchased from the distribution network at time t, is the natural gas price at time t.

[0059] The carbon trading cost of energy hub i is related to its carbon emissions and carbon emission quota, which can be expressed as:

[0060]

[0061] Where m is the type of carbon emission source in energy hub i, mainly including purchased electricity, gas turbines and gas boilers. and are the carbon emissions and carbon emission quota of the mth carbon emission source in energy hub i at time t, as shown below:

[0062] Among them, purchased electricity is regarded as thermal power generation, and its carbon emission quota can be expressed as:

[0063] D e =λ e P e

[0064] Where D e Carbon quota allocated for purchased electricity, λ e is the carbon quota coefficient per unit of electricity production, P e For purchased electricity.

[0065] Gas turbines provide additional heat while generating electricity, so their carbon quota can be expressed as:

[0066] D gt =λ h (c eh P gt +H gt )

[0067] Where D gt Carbon quota allocated to gas turbines, λ h is the carbon quota coefficient per unit heat production, c eh is the conversion factor of electricity into heat, P gt and H gt are the electricity and heat production of the gas turbine respectively.

[0068] The gas boiler is only used to provide heat, so its carbon quota can be expressed as:

[0069] D gb =λ h H gb

[0070] Where D gb Carbon quota allocated to gas boilers, H gb The heat output of the gas boiler.

[0071] The actual carbon emissions of purchased electricity, gas turbines, and gas boilers can be expressed as models proportional to electricity and heat, respectively:

[0072] A e =ε e P e

[0073] A gt =ε h (c eh P gt +H gt )

[0074] A gb =ε h H gb

[0075] In the formula, A e , A gt and A gb are the carbon emissions generated by purchased electricity, gas turbines and gas boilers, ε e It is the carbon emission coefficient of coal-fired power plants that produce unit power. After converting the power into heat, the equivalent heating capacity of gas turbines and gas boilers is close.

[0076] 2. Constraints

[0077] (1) Equipment output constraints:

[0078] (a) Gas turbine

[0079]

[0080] In the formula, and are the power generation and heat generation of the gas turbine at time t, and are the minimum and maximum technical outputs of gas turbines for electricity generation, respectively; and are the minimum and maximum technical outputs of gas turbine heat production, respectively.

[0081] (b) Gas boiler

[0082]

[0083] In the formula, is the heating power of the gas boiler at time t; and are the maximum and minimum heat generation powers respectively.

[0084] (c) Electric Refrigerator

[0085]

[0086] In the formula, is the cooling power of the electric refrigerator at time t; and are the minimum and maximum cooling power of the electric refrigerator respectively.

[0087] (d) Absorption Refrigerator

[0088]

[0089] In the formula, is the refrigeration coefficient of the absorption chiller, and are the minimum and maximum cooling power of the absorption chiller respectively.

[0090] (e) Energy storage device

[0091]

[0092] In the formula, the subscript x is the type of energy, which in this embodiment is electrical energy and thermal energy. is the energy stored at time t, θ x is the energy self-loss rate, η x,c and η x,d are the energy storage and release efficiencies, and are the energy storage and release powers, respectively. and are the maximum energy storage and release power, u x It is a 0-1 variable introduced to ensure that energy storage and energy release cannot be carried out at the same time. and They are the maximum storage capacity and minimum storage capacity of the energy storage device respectively. To facilitate the scheduling and management of the energy storage device, the last item ensures that the stored energy before and after the scheduling cycle is the same.

[0093] (2) Energy balance constraint (the superscript t is omitted here for simplicity):

[0094]

[0095] Where, L e,i , L h,i and L c,i are the electrical, heating and cooling loads of energy hub i respectively; η wt,i , η gb,i , COP ec,i and COP ac,i are the energy conversion efficiency of the corresponding energy conversion device; P e,i , P wt,i , G gt,i , Ggb,i , P ec,i , H ac,i are the purchased electricity and the input power of the corresponding energy conversion device respectively; P ij and H ij are the electrical energy and thermal energy transmitted from energy hub i to energy hub j, respectively, and P ji and H ji They are the electrical energy and thermal energy transmitted from energy hub j to energy hub i, respectively.

[0096] (3) Transmission power constraints of tie lines

[0097] (a) Transmission power constraints with the upper network

[0098]

[0099]

[0100] In the formula, and is the maximum transmission power of the electric power tie line and the natural gas tie line. This constraint is used to ensure that the power transmitted by the tie line does not exceed its limit.

[0101] (b) Transmission power constraints between energy hubs

[0102]

[0103] In the formula, is the maximum transmission power constraint of the power connection line between energy hubs i and j, and Respectively represent the direction of power transmission between energy hubs i and j, When it is 1, it means that the electric energy is transferred from i to j. When it is 1, it indicates that electric energy is transferred from j to i.

[0104]

[0105] In the formula, is the maximum transmission power constraint of the thermal energy tie line between energy hubs i and j, and Respectively represent the direction of heat transfer between energy hubs i and j, When it is 1, it means that heat energy is transferred from i to j. When it is 1, it means that heat energy is transferred from j to i.

[0106] So far, a multi-agent cooperative game model of the multi-energy hub integrated energy system has been established, and the CPLEX solver is used to solve the 24-hour output of each unit in each energy hub to achieve optimal scheduling of the integrated energy system. In order to verify the advantages of introducing carbon trading and considering power interaction in the multi-energy hub IES, the following four scenarios are set up:

[0107] Scenario 1: Without considering the carbon trading mechanism, the three energy hubs operate independently, and there is no interaction of electrical energy and thermal energy between the energy hubs.

[0108] Scenario 2: Considering the carbon trading mechanism, the three energy hubs operate independently, and there is no interaction of electrical energy and thermal energy between the energy hubs.

[0109] Scenario 3: Without considering the carbon trading mechanism, the three energy hubs work together to exchange electrical and thermal power through electrical and thermal interconnection lines.

[0110] Scenario 4: Considering the carbon trading mechanism, three energy hubs work together to exchange electrical and thermal power through electrical and thermal interconnection lines.

[0111] The optimization scheduling results under different scenarios are shown in Table 1.

[0112] Table 1 Optimization scheduling results under different scenarios

[0113]

[0114] (1) Analysis of the impact of power interaction between energy hubs on scheduling results

[0115] Comparing scenarios 1-3 and 2-4, it can be seen that when the energy hubs cooperate with each other, that is, when they can exchange power through the interconnection line, the operating costs are reduced by 1,089 yuan and 1,078 yuan respectively compared with independent operation. This is because when in independent operation mode, the energy supply mainly depends on the supply capacity of each energy hub, while in the cooperative mode, energy sharing is achieved through the interaction of power between energy hubs, such as Figure 4(a)-Figure 4(c) , Figure 5(a)-Figure 5(c) As shown by the transmission power between energy hubs, energy use is more reasonable, thus reducing the operating cost of the entire system. Compared with scenarios 1 and 3, when power interaction is taken into account, carbon emissions increase instead, which is caused by the increase in electricity purchase; while compared with scenarios 2 and 4, carbon emissions are significantly reduced, which also fully demonstrates the necessity of introducing a carbon trading mechanism.

[0116] (2) Whether to consider the impact of carbon trading on scheduling results

[0117] Comparing scenarios 1 and 2 with scenarios 3 and 4, when considering the carbon trading mechanism, the operating costs were reduced by 2,139 yuan and 2,127 yuan respectively, and the carbon emissions were reduced by 5.3 tons and 7 tons respectively, achieving a win-win situation in terms of economy and environmental protection. As can be seen from Table 1, this is because when considering carbon trading, the system will be more inclined to use cleaner gas turbines to supply electricity, so the amount of electricity purchased from the upper power grid will decrease, while the amount of gas purchased will increase, so carbon emissions will decrease. Figure 4(a)-Figure 4(c) It can be seen that after the introduction of the carbon trading mechanism, electricity is mainly met by wind turbines and gas turbines, and less electricity is purchased from the upper power grid, resulting in the carbon emissions of the entire system being less than the carbon quota. The system can sell carbon quotas to obtain income. The carbon trading costs corresponding to scenarios two and four in Table 1 are negative, so the operating costs of the system are reduced compared to when carbon trading is not considered.

[0118] Taking scenario 4 as an example, the scheduling strategy of the multi-energy hub IES is analyzed in detail.

[0119] Figure 4(a)-Figure 4(c) The power dispatch strategy of the multi-energy hub IES considering carbon trading in the cooperative mode is to keep the power balanced at any time in the dispatch cycle. Figure 4(a), Figure 4(b), and Figure 4(c) are the power optimization operation results of energy hubs 1, 2, and 3, respectively. It can be seen that the power load of each energy hub is mainly met by wind turbines and gas turbines, and the insufficient part is met by the supply of other energy hubs and the purchase of power from the power grid. Among them, the time period for each energy hub to purchase power from the upper power grid is concentrated at 23:00 and 24:00, because the power load demand at this time is large and the electricity price is at a low point. Taking energy hub 1 as an example, the battery is charged when the electricity price is low and the power supply is surplus from 5:00 to 7:00, and discharged when the electricity price is high from 8:00 to 9:00 and 14:00 and there is a shortage of electricity, realizing the transfer of electricity in time. In addition, from 3:00 to 9:00, the power load demand of energy hub 1 is high, so other energy hubs in the system transmit power to it. From 10:00 to 11:00, the power load demand of energy hub 1 is low, so the excess power generated can be transmitted to other energy hubs in the system through the power grid. The analysis of other time periods and other energy hubs in the system is similar to the above analysis, which will not be repeated here. Therefore, through the mutual transmission of power between the various entities in the system, the complementary power is achieved, thereby increasing the benefits.

[0120] Figure 5(a)-Figure 5(c)The thermal energy scheduling strategy of the multi-energy hub IES considering carbon trading in the cooperative mode is used to keep the thermal energy balanced at any time in the scheduling cycle. Figures 5(a), 5(b), and 5(c) are the thermal energy optimization operation results of energy hubs 1, 2, and 3, respectively. It can be seen that the thermal load of each energy hub is mainly met by the gas turbine, and the insufficient part is met by the thermal energy transmitted by the gas boiler and other energy hubs in the system. As can be seen from Figure 5(a), at 3:00 and 5:00, the output of the gas turbine is low, and the heat generated is not enough to meet the thermal load, so the gas boiler is started to generate heat. At 19:00 and 21:00-22:00, the output of the gas turbine in energy hub 1 is higher than the thermal load demand, and the heat storage device is in a heat absorption state at this time. At 9:00, 12:00-14:00, and 20:00, the output of the gas turbine is not enough to meet the thermal load, which is met by the heat storage device releasing heat. The heat storage device realizes the role of energy buffering in the system. In addition, the gas turbine output is high from 1:00 to 2:00 and exceeds the heat load demand, so the remaining heat energy can be transported to other energy hubs in the system through the heat network to achieve complementary energy utilization. The other time periods and energy hubs 2 and 3 are the same as the above analysis. Because the mutual transmission of cold energy between energy hubs is not considered, the cold energy balance of the system is no longer analyzed separately.

[0121] Analysis of profit distribution strategy

[0122] Cooperative games can be used to solve the problem of interest distribution. In the above model, each energy hub can be regarded as a participant in the game problem. There can be seven combinations between the three energy hubs: {EH1}, {EH2}, {EH3}, {EH1,EH2}, {EH1,EH3}, {EH2,EH3}, {EH1,EH2,EH3}. Each combination is regarded as an alliance. The above seven combinations can be divided into three operating modes: non-alliance, partial alliance and full alliance. The operating costs and benefits (reduced costs) of the seven combinations are calculated respectively, as shown in Table 2.

[0123] Table 2 Cost savings under different operating modes

[0124]

[0125] As can be seen from Table 2, the costs of all partial alliances and full alliances are reduced compared to non-alliance, and the highest benefit is achieved when the three energy hubs form a full alliance, with a cost reduction of 1,078 yuan. Based on the above results, the benefits of each participant under different allocation strategies can be calculated as shown in Table 3:

[0126] Table 3 Benefits of each participant under different allocation strategies

[0127]

[0128]

[0129] In order to verify the stability of the above three allocation schemes, the DP index under different allocation strategies is calculated, as shown in Table 4:

[0130] Table 4 DP indicators under different allocation strategies

[0131]

[0132] In the Shapley value allocation strategy, the DP value of EH2 is negative. From the above analysis, it can be seen that although it satisfies overall rationality and individual rationality, it cannot satisfy alliance rationality. Therefore, this alliance is unstable. EH1 and EH3 tend to not accept this allocation strategy and form a new alliance {EH1, EH3} to obtain greater benefits. In the allocation strategy of the nucleolus method, although the DP value of EH3 can take the minimum value, indicating that EH3's satisfaction is the highest at this time, the DP value of EH2 is close to 1 at this time. In other words, at this time, the loss caused to other participants by EH2's refusal to accept this allocation is almost equal to the loss caused to itself. At this time, EH2 may accept this allocation strategy or threaten other participants by refusing to cooperate to obtain higher benefits. Therefore, it is also unstable. In the equal DP allocation strategy, the DP value of each participant is a positive number greater than 0 and less than 1. In other words, under this allocation strategy, each participant cannot cause greater losses to other participants by refusing to participate in cooperation, and the satisfaction with the allocation strategy is the same at this time, so it is stable.

[0133] This embodiment first determines the carbon emission sources in the system, including purchased electricity, gas turbines, and gas boilers, where purchased electricity is considered to be generated by coal-fired units. Then, carbon quotas are allocated free of charge to units participating in carbon trading. If the carbon emissions of a unit exceed the corresponding carbon quota, it is necessary to purchase carbon emission rights from the carbon trading market; if the carbon emissions of a unit are lower than the corresponding carbon quota, it can sell the corresponding carbon emission rights to the carbon trading market to obtain income. Thus, by adopting carbon trading, the carbon emissions of the system are reduced, and the low-carbon operation of IES is promoted.

[0134] Embodiment 2

[0135] This embodiment provides a low-carbon scheduling system for a multi-energy hub integrated energy system.

[0136] A multi-energy hub integrated energy system low-carbon dispatching system, comprising:

[0137] A model building module is configured to: construct an objective function according to the electricity purchase cost, gas purchase cost and carbon trading cost of the multi-energy hub integrated energy system, and construct a multi-agent cooperative game model in combination with equipment output constraints, energy balance constraints and interconnection line transmission power constraints;

[0138] A data acquisition module, which is configured to: acquire load, energy price, carbon trading price and equipment parameters;

[0139] The revenue calculation module is configured to: based on load, energy price, carbon trading price and equipment parameters, adopt a multi-agent cooperative game model to solve the optimal dispatching strategy, as well as the revenue of each agent when there is no alliance, the alliance revenue when there is a partial alliance and the total revenue when there is a full alliance;

[0140] The income processing module is configured to: use different allocation strategies to process the income of each subject when there is no alliance, the alliance income when there is a partial alliance, and the total income when there is a full alliance, so as to obtain the income of each subject in the integrated energy system;

[0141] The verification module is configured to: use the DP indicator to verify the stability of different allocation strategies, select the optimal allocation strategy to take into account the interests of each subject in the system and reduce the operating costs and carbon emissions of the system.

[0142] It should be noted that the above-mentioned model building module, data acquisition module, income calculation module, income processing module and verification module are the same as the examples and application scenarios implemented by the steps in Embodiment 1, but are not limited to the contents disclosed in Embodiment 1. It should be noted that the above-mentioned modules as part of the system can be executed in a computer system such as a set of computer executable instructions.

[0143] Embodiment 3

[0144] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps in the low-carbon scheduling method for a multi-energy hub integrated energy system as described in the first embodiment above are implemented.

[0145] Embodiment 4

[0146] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the low-carbon scheduling method of a multi-energy hub integrated energy system as described in the first embodiment above are implemented.

[0147] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0148] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0149] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0150] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0151] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0152] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A low-carbon scheduling method for a multi-energy hub integrated energy system, characterized in that: include: The objective function is constructed based on the electricity purchase cost, gas purchase cost and carbon trading cost of the multi-energy hub integrated energy system, and a multi-agent cooperative game model is constructed by combining the equipment output constraints, energy balance constraints and interconnection line transmission power constraints. The multi-energy hub integrated energy system is composed of three energy hubs, and the three energy hubs can exchange electric and thermal power with each other; the energy hub is composed of energy conversion equipment and energy storage equipment, wherein the energy conversion equipment includes fans, gas turbines, gas boilers, electric refrigerators, absorption refrigerators, and the energy storage equipment includes batteries and heat storage devices; the electric load is met by the purchase of electricity from the upper power grid, gas turbines, batteries, and the exchange of electric loads between the multi-energy hubs; the thermal load is met by the exchange of thermal loads between gas turbines, gas boilers, heat storage devices, and the multi-energy hubs; the cold load is met by electric refrigerators and absorption refrigerators; The objective function is: In the formula, is the total operating cost of the integrated energy system of the multi-energy hub, is the number of energy hubs, For energy hubs The cost of purchasing electricity, For energy hubs The gas purchase cost, For energy hubs carbon trading costs; Energy Hub The carbon trading cost of a country is related to its carbon emissions and carbon emission quota, which can be expressed as: In the formula, For energy hubs Types of carbon emission sources include purchased electricity, gas turbines and gas boilers. and They are Moment Energy Hub Middle The carbon emissions and carbon emission quotas of various carbon emission sources are as follows: Among them, purchased electricity is regarded as thermal power generation, and its carbon emission quota is expressed as: In the formula, Carbon allowances allocated for purchased electricity, is the carbon quota coefficient per unit of electricity production, For purchased electricity; The gas turbine generates electricity and also provides heat. Its carbon quota is expressed as: In the formula, Carbon quotas allocated for gas turbines, is the carbon quota coefficient per unit heat production, is the conversion factor of electricity into heat, and are the electricity and heat production of the gas turbine respectively; Gas boilers are used only to provide heat, so their carbon allowance is expressed as: In the formula, Carbon allowances allocated for gas boilers, is the heat output of the gas boiler; The actual carbon emissions of purchased electricity, gas turbines, and gas boilers are expressed as models proportional to the amount of electricity and heat, respectively: In the formula, , and are the carbon emissions generated by purchased electricity, gas turbines and gas boilers, is the carbon emission coefficient of a coal-fired power plant per unit of power generated; After converting the electricity into heat, the equivalent heating capacity of the gas turbine and the gas boiler is close; The equipment output constraints include: power generation power constraints and heat generation power constraints of gas turbines, heat generation power constraints of gas boilers, refrigeration power constraints of electric refrigerators, refrigeration power constraints of absorption refrigerators and energy storage device constraints; wherein the energy storage device constraints include stored energy, energy storage power constraints, energy release power constraints and storage capacity constraints of energy storage devices; The energy balance constraints include: electric energy balance constraints, thermal energy balance constraints and cold energy balance constraints, wherein the energy balance constraints are described using an energy hub matrix model, and the formula is as follows: In the formula, , and Energy Hub The electricity, heating and cooling loads; , , , , and are the energy conversion efficiencies of the corresponding energy conversion devices; , , , , , are the purchased electricity and the input power of the corresponding energy conversion device respectively; and Energy Hub Energy Hub The electrical and thermal energy transmitted, and Energy Hub Energy Hub Electrical and thermal energy transmitted; The transmission power constraint of the tie line includes: transmission power constraint with the upper network and transmission power constraint between energy hubs; wherein the transmission power constraint with the upper network is: In the formula, and is the maximum transmission power of the electric power tie line and the natural gas tie line. This constraint is used to ensure that the power transmitted by the tie line does not exceed its limit; The transmission power constraint between energy hubs is: In the formula, For energy hubs and The maximum transmission power constraint of the power tie line between them, and Energy Hub and The direction of electrical energy transmission between , The energy hubs at time t are Energy Hub Transmitting electrical power, energy hub at time t Energy Hub Transmit electrical power; When it is 1, it means that the electric energy is Towards transmission, When it is 1, it means that the electric energy is Towards transmission; In the formula, For energy hubs and The maximum transmission power constraint of the thermal energy interconnection line between them, and Energy Hub and The direction of heat transfer between , The energy hubs at time t are Energy Hub Transfer heat power, energy hub at time t Energy Hub Transfer heat power; When it is 1, it means that the heat energy is Towards transmission, When it is 1, it means that the heat energy is Towards transmission; Obtain load, energy price, carbon trading price and equipment parameters; Based on load, energy price, carbon trading price and equipment parameters, a multi-agent cooperative game model is used to solve the optimal dispatching strategy, as well as the benefits of each agent when there is no alliance, the alliance benefits when there is a partial alliance, and the total benefits when there is a complete alliance. Different allocation strategies are used to process the income of each subject when there is no alliance, the alliance income when there is a partial alliance, and the total income when there is a complete alliance, so as to obtain the income of each subject in the integrated energy system; the allocation strategies include Shapley value method, kernel method and equal DP method; The DP indicator is used to verify the stability of different allocation strategies, and the optimal allocation strategy is selected to take into account the interests of each entity in the system and reduce the operating costs and carbon emissions of the system.

2. The low-carbon scheduling method for a multi-energy hub integrated energy system according to claim 1 is characterized in that: The profit of each entity when there is no alliance, the alliance profit when there is partial alliance and the total profit when there is full alliance are respectively the profit of each energy hub when the energy hub operates alone, the alliance profit when some energy hubs operate in cooperation and the total profit when all energy hubs operate in full cooperation.

3. A low-carbon dispatching system for a multi-energy hub integrated energy system, characterized in that: include: A model building module is configured to: construct an objective function according to the electricity purchase cost, gas purchase cost and carbon trading cost of the multi-energy hub integrated energy system, and construct a multi-agent cooperative game model in combination with equipment output constraints, energy balance constraints and interconnection line transmission power constraints; The multi-energy hub integrated energy system is composed of three energy hubs, and the three energy hubs can exchange electric and thermal power with each other; the energy hub is composed of energy conversion equipment and energy storage equipment, wherein the energy conversion equipment includes fans, gas turbines, gas boilers, electric refrigerators, absorption refrigerators, and the energy storage equipment includes batteries and heat storage devices; the electric load is met by the purchase of electricity from the upper power grid, gas turbines, batteries, and the exchange of electric loads between the multi-energy hubs; the thermal load is met by the exchange of thermal loads between gas turbines, gas boilers, heat storage devices, and the multi-energy hubs; the cold load is met by electric refrigerators and absorption refrigerators; The objective function is: In the formula, is the total operating cost of the integrated energy system of the multi-energy hub, is the number of energy hubs, For energy hubs The cost of purchasing electricity, For energy hubs The gas purchase cost, For energy hubs carbon trading costs; Energy Hub The carbon trading cost of a country is related to its carbon emissions and carbon emission quota, which can be expressed as: In the formula, For energy hubs Types of carbon emission sources include purchased electricity, gas turbines and gas boilers. and They are Moment Energy Hub Middle The carbon emissions and carbon emission quotas of various carbon emission sources are as follows: Among them, purchased electricity is regarded as thermal power generation, and its carbon emission quota is expressed as: In the formula, Carbon allowances allocated for purchased electricity, is the carbon quota coefficient per unit of electricity production, For purchased electricity; The gas turbine generates electricity and also provides heat. Its carbon quota is expressed as: In the formula, Carbon quotas allocated for gas turbines, is the carbon quota coefficient per unit heat production, is the conversion factor of electricity into heat, and are the electricity and heat production of the gas turbine respectively; Gas boilers are used only to provide heat, so their carbon allowance is expressed as: In the formula, Carbon allowances allocated for gas boilers, is the heat output of the gas boiler; The actual carbon emissions of purchased electricity, gas turbines, and gas boilers are expressed as models proportional to the amount of electricity and heat, respectively: In the formula, , and are the carbon emissions generated by purchased electricity, gas turbines and gas boilers, is the carbon emission coefficient of a coal-fired power plant per unit of power generated; After converting the electricity into heat, the equivalent heating capacity of the gas turbine and the gas boiler is close; The equipment output constraints include: power generation power constraints and heat generation power constraints of gas turbines, heat generation power constraints of gas boilers, refrigeration power constraints of electric refrigerators, refrigeration power constraints of absorption refrigerators and energy storage device constraints; wherein the energy storage device constraints include stored energy, energy storage power constraints, energy release power constraints and storage capacity constraints of energy storage devices; The energy balance constraints include: electric energy balance constraints, thermal energy balance constraints and cold energy balance constraints, wherein the energy balance constraints are described using an energy hub matrix model, and the formula is as follows: In the formula, , and Energy Hub The electricity, heating and cooling loads; , , , , and are the energy conversion efficiencies of the corresponding energy conversion devices; , , , , , are the purchased electricity and the input power of the corresponding energy conversion device respectively; and Energy Hub Energy Hub The electrical and thermal energy transmitted, and Energy Hub Energy Hub Electrical and thermal energy transmitted; The transmission power constraint of the tie line includes: transmission power constraint with the upper network and transmission power constraint between energy hubs; wherein the transmission power constraint with the upper network is: In the formula, and is the maximum transmission power of the electric power tie line and the natural gas tie line. This constraint is used to ensure that the power transmitted by the tie line does not exceed its limit; The transmission power constraint between energy hubs is: In the formula, For energy hubs and The maximum transmission power constraint of the power tie line between them, and Energy Hub and The direction of electrical energy transmission between , The energy hubs at time t are Energy Hub Transmitting electrical power, energy hub at time t Energy Hub Transmit electrical power; When it is 1, it means that the electric energy is Towards transmission, When it is 1, it means that the electric energy is Towards transmission; In the formula, For energy hubs and The maximum transmission power constraint of the thermal energy interconnection line between them, and Energy Hub and The direction of heat transfer between , are the energy hubs at time t Energy Hub Transfer heat power, energy hub at time t Energy Hub Transfer heat power; When it is 1, it means that the heat energy is Towards transmission, When it is 1, it means that the heat energy is Towards transmission; A data acquisition module, which is configured to: acquire load, energy price, carbon trading price and equipment parameters; The revenue calculation module is configured to: based on load, energy price, carbon trading price and equipment parameters, adopt a multi-agent cooperative game model to solve the optimal dispatching strategy, as well as the revenue of each agent when there is no alliance, the alliance revenue when there is a partial alliance and the total revenue when there is a full alliance; The income processing module is configured to: use different allocation strategies to process the income of each subject when there is no alliance, the alliance income when there is a partial alliance, and the total income when there is a full alliance, so as to obtain the income of each subject in the integrated energy system; the allocation strategies include Shapley value method, nucleolus method and equal DP method; The verification module is configured to: use the DP indicator to verify the stability of different allocation strategies, select the optimal allocation strategy to take into account the interests of each subject in the system and reduce the operating costs and carbon emissions of the system.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the low-carbon scheduling method of a multi-energy hub integrated energy system as described in any one of claims 1-2 are implemented.

5. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the low-carbon scheduling method of the multi-energy hub integrated energy system as described in any one of claims 1-2 are implemented.

Images