Optimal operation method and system for multi-agent integrated energy system considering energy sharing

Abstract

The present invention belongs to the field of integrated energy systems, and provides a method and system for optimizing the operation of a multi-agent integrated energy system that takes energy sharing into consideration, including analyzing the mutual coupling mechanism of multiple energy sources such as electricity, heat, gas, and hydrogen, and introducing a step-by-step carbon trading mechanism to construct an integrated energy system; establishing a master-slave game model on both the supply and demand sides based on the mathematical models of energy suppliers, energy operators, and distributed prosumers; and using a two-layer optimization algorithm to solve the master-slave game model on both the supply and demand sides to obtain the optimal operation strategy of the integrated energy system. Based on the master-slave game, the present invention takes the distributed prosumer group as followers, and the energy supplier and energy operator as leaders, solves their equilibrium interaction strategy, and realizes the collaborative optimization operation of multiple subjects.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated energy systems, and specifically relates to an optimized operation method and system for a multi-agent integrated energy system taking energy sharing into consideration. 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] The integrated energy system can give full play to the complementary and mutually beneficial characteristics of various energy sources, improve the utilization rate of electricity, heat, gas and other energy sources through the cascade utilization of energy, and optimize the low-carbon economy of the multi-energy system. The coupling of hydrogen energy and other energy sources in the integrated energy system to form a hydrogen-containing integrated energy system is more prominent in the diversity of energy utilization forms compared to the traditional integrated energy system with electric energy storage. It can convert surplus electricity into hydrogen, heat and other energy forms to meet different terminal needs. It has shown obvious advantages in environmental protection and economy, and has become a research hotspot.

[0004] There are multiple types of stakeholders and large-scale penetration of renewable energy in the hydrogen-containing integrated energy system, which leads to the uncertainty of multi-subject distributed resources. Coordinating the output behavior of multi-subject distributed resources and efficiently utilizing distributed resources to achieve multi-subject energy sharing is an effective way to solve this problem. As more and more consumers on the demand side transform into prosumers who can both produce and consume electricity, their load demands are not the same, and the distributed renewable energy devices configured themselves have the characteristics of small capacity, random output, and diverse ways of accessing the prosumer group, forming heterogeneous characteristics within the prosumer group. Therefore, there will be differences in the spatiotemporal distribution of energy within the prosumer group.

[0005] At present, in the context of hydrogen-containing integrated energy systems, there is still a gap in the research on distributed prosumer energy sharing models. In addition, there are few literatures studying the application of carbon trading in hydrogen-containing integrated energy systems, especially in solving the energy sharing problem of such systems. Summary of the invention

[0006] In order to solve the above problems, the present invention proposes a method and system for optimizing the operation of a multi-agent integrated energy system considering energy sharing. The present invention considers a stepped carbon trading mechanism in the problem of optimizing the operation of a hydrogen-containing integrated energy system; ensures that distributed producers and consumers give full play to their autonomy, flexibly choose trading methods, and adjust load demand response under the time-sharing energy price mechanism, so that they can flexibly use energy and maximize surplus while meeting the comfort level; introduces master-slave game to optimize the multi-agent energy management strategy and trading operation strategy on both the supply and demand sides, and verifies the effectiveness of the proposed method in enhancing the environmental protection, economy and autonomy of the hydrogen-containing integrated energy system.

[0007] According to some embodiments, a first solution of the present invention provides a multi-agent integrated energy system optimization operation method considering energy sharing, which adopts the following technical solutions:

[0008] The optimal operation method of multi-agent integrated energy system considering energy sharing includes:

[0009] Analyze the mutual coupling mechanism of electricity, heat, gas and hydrogen and introduce a step-by-step carbon trading mechanism to build a comprehensive energy system;

[0010] Based on the mathematical models of energy suppliers, energy operators and distributed prosumers, a master-slave game model on both the supply and demand sides is established;

[0011] A two-layer optimization algorithm is used to solve the master-slave game model on both the supply and demand sides to obtain the optimal operation strategy of the integrated energy system.

[0012] According to some embodiments, the second solution of the present invention provides a multi-agent integrated energy system optimization operation system considering energy sharing, which adopts the following technical solutions:

[0013] The multi-agent integrated energy system optimization operation system considering energy sharing includes:

[0014] The integrated energy system building module is configured to analyze the mutual coupling mechanism of electricity, heat, gas and hydrogen and introduce a step-by-step carbon trading mechanism to build an integrated energy system;

[0015] A master-slave game model building module is configured to build a master-slave game model on both the supply and demand sides based on the mathematical models of energy suppliers, energy operators and distributed prosumers;

[0016] The model solving module is configured to use a two-layer optimization algorithm to solve the master-slave game model on both the supply and demand sides to obtain the optimal operation strategy of the integrated energy system.

[0017] According to some embodiments, a third aspect of the present invention provides a computer-readable storage medium.

[0018] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in the method for optimizing the operation of a multi-agent integrated energy system considering energy sharing as described in the first aspect above.

[0019] According to some embodiments, a fourth aspect of the present invention provides a computer device.

[0020] 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 method for optimizing operation of a multi-agent integrated energy system taking into account energy sharing as described in the first aspect above are implemented.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] Based on the master-slave game, the present invention takes the distributed prosumer group as followers, and the energy supplier and energy operator as leaders, solves their equilibrium interaction strategy, and realizes the collaborative optimization operation of multiple subjects. In the optimization operation problem of the hydrogen-containing integrated energy system, the step-by-step carbon trading mechanism is considered to effectively achieve carbon emission reduction and ensure the environmental protection of the system operation. Distributed prosumers are allowed to give full play to their autonomy, flexibly choose the trading method, and adjust the load demand response under the time-sharing energy price mechanism, so that they can use energy flexibly and maximize the surplus while meeting the comfort level, thereby promoting the local consumption of renewable energy. The master-slave game is introduced to optimize the multi-subject energy management strategy and transaction operation strategy on both the supply and demand sides, increase the benefits of each subject in the integrated energy system, achieve mutual benefit and win-win results for each subject, and make the overall economic effect of the system more obvious. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] 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.

[0024] Figure 1 It is a schematic diagram of the architecture of the hydrogen-containing integrated energy system proposed in the present invention;

[0025] Figure 2 It is a schematic diagram of the integrated energy market architecture proposed in the present invention;

[0026] Figure 3 It is the algorithm flow chart proposed by the algorithm flow chart of the present invention;

[0027] Figure 4 It is a diagram of the balanced convergence results in the example analysis of the present invention;

[0028] FIG5( a ) is a diagram of the electricity price strategy of the integrated energy supplier in the example analysis of the present invention;

[0029] FIG5( b ) is a diagram of the heat price strategy of the integrated energy supplier in the example analysis of the present invention;

[0030] Figure 6 It is a diagram of the internal pricing strategy of the distributed prosumer in the example analysis of the present invention;

[0031] Figure 7 It is the load optimization curve of the prosumer before and after energy sharing in the example analysis of the present invention;

[0032] FIG8( a ) is a diagram showing the results of electricity trading for commercial prosumer 1 in the example analysis of the present invention;

[0033] FIG8( b ) is a diagram showing the results of electricity trading for commercial prosumer 2 in the example analysis of the present invention;

[0034] FIG8( c ) is a diagram showing the results of residential prosumer electricity trading in the example analysis of the present invention;

[0035] FIG8( d ) is a diagram showing the results of industrial prosumer power trading in the example analysis of the present invention;

[0036] FIG9( a ) is a diagram of the power balance of electric loads in the example analysis of the present invention;

[0037] FIG9( b ) is a heat load power balance diagram in the example analysis of the present invention;

[0038] FIG9( c ) is a gas load power balance diagram in the example analysis of the present invention;

[0039] FIG9( d ) is a hydrogen load power balance diagram in the example analysis of the present invention. DETAILED DESCRIPTION

[0040] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0041] It should be noted that the following detailed descriptions are all illustrative and 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.

[0042] 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.

[0043] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0044] Embodiment 1

[0045] like Figure 1As shown, this embodiment provides a method for optimizing the operation of a multi-agent integrated energy system taking into account energy sharing. This embodiment uses the method applied to a server as an example. 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 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 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:

[0046] Analyze the mutual coupling mechanism of electricity, heat, gas and hydrogen and introduce a step-by-step carbon trading mechanism to build a comprehensive energy system;

[0047] Based on the mathematical models of energy suppliers, energy operators and distributed prosumers, a master-slave game model on both the supply and demand sides is established;

[0048] A two-layer optimization algorithm is used to solve the master-slave game model on both the supply and demand sides to obtain the optimal operation strategy of the integrated energy system.

[0049] A low-carbon economic operation method for a multi-agent hydrogen-containing integrated energy system considering energy sharing comprises the following steps:

[0050] When constructing an integrated energy system, the mutual coupling mechanism of electricity, heat, gas, and hydrogen is analyzed and a step-by-step carbon trading mechanism is introduced. The system mainly includes four parts: energy supply side, multi-energy coupling link, energy storage link, and demand side. The energy supply side is mainly composed of power grid, gas grid, centralized wind power, photovoltaic devices, etc. The equipment in the multi-energy coupling link mainly includes electrolyzer, methane generator, hydrogen fuel cell, gas turbine, gas boiler, etc. The energy storage link mainly includes electric energy storage and hydrogen energy storage, etc. The demand side is mainly composed of residential, commercial, and industrial prosumers equipped with different capacities. Among them, the electric load is supplied by its own photovoltaic equipment, cogeneration units, hydrogen fuel cells, and wind, light, and storage equipment. The insufficient part is supplemented by the external power grid. The thermal load is supplied by the cogeneration units and gas boilers, the gas load is supplied by the methane generator and the external gas grid, and the hydrogen load is supplied by the electrolyzer and hydrogen storage equipment.

[0051] Based on the mathematical models of integrated energy suppliers, integrated energy operators and distributed prosumers, a master-slave game model on both the supply and demand sides was established. The energy operator is regarded as the leader of the distributed prosumer group, the clearing results of the distributed prosumer group are counted, and the transaction price curve within the distributed prosumer group is given. The distributed prosumer group adjusts the electric and thermal loads traded between each time period to achieve energy sharing among multiple entities on the demand side; the energy operator, as a link between the energy supplier and the distributed prosumer group, and as a follower of the energy supplier, will feedback the clearing results to the energy supplier when the distributed prosumer group is in short supply. The insufficient energy will be supplemented by the energy supplier. The energy supplier determines the optimal output and sets the electricity and heat prices to maximize the benefits of each market entity.

[0052] The differential evolution algorithm is combined with the double-layer optimization algorithm of the CPLEX solver to solve the optimal operation strategy of the system. Finally, the advantages of the proposed operation strategy in carbon emission reduction, flexible energy use, and improving the benefits of various entities are verified through actual examples.

[0053] The comprehensive energy system constructed in this example mainly includes four parts, the energy supply side, the multi-energy coupling link, the energy storage link and the demand side. The energy supply side is mainly composed of the power grid, gas grid, centralized wind power, photovoltaic devices, etc. The equipment in the multi-energy coupling link mainly includes electrolyzers, methane generators, hydrogen fuel cells, gas turbines, gas boilers, etc. The energy storage link mainly includes electric energy storage and hydrogen energy storage, etc. The demand side is mainly composed of residential, commercial and industrial prosumers equipped with different capacities. Among them, the electric load is supplied by its own photovoltaic equipment, cogeneration units, hydrogen fuel cells and wind and solar storage equipment, and the insufficient part is supplemented by the external power grid. The heat load is supplied by the cogeneration units and gas boilers, the gas load is supplied by the methane generator and the external gas grid, and the hydrogen load is supplied by the electrolyzer and hydrogen storage equipment. The system can simultaneously meet the user's different energy needs such as electricity, heat, gas and hydrogen. Its structural diagram is shown as follows Figure 1 shown.

[0054] During the operation of the multi-agent hydrogen-containing integrated energy system, the prosumer cluster first uses its own distributed photovoltaic resources for energy sharing. During this period, excess electricity can be directly sold to surrounding distributed prosumers, and insufficient electricity can also be purchased from surrounding distributed prosumers. For example, residential prosumers have a small electricity load demand during the day and have surplus electricity, while industrial prosumers have a daytime electricity load demand that is much higher than their own photovoltaic supply and have insufficient electricity. Residential prosumers can sell surplus electricity directly to industrial prosumers. When the distributed photovoltaics within the prosumer group cannot meet the group's electricity load, it will be replenished by external wind, solar and storage equipment, cogeneration units, hydrogen fuel cells or external power grids.

[0055] 1. Energy trading process

[0056] Figure 2As a comprehensive energy market architecture model, this article will consist of three trading entities: energy suppliers, energy operators, and distributed prosumers. The energy suppliers are composed of wind power, photovoltaic devices, energy conversion equipment, and energy storage. The prosumers are composed of residential, industrial, and commercial prosumers on the demand side. Energy operators, as the "middleman" between energy suppliers and the demand side, refer to the electricity and heat market prices to formulate energy purchase prices for energy suppliers and energy sales prices for distributed prosumers with the goal of maximizing their own profits.

[0057] During the operation process, the energy operator, as the leader of the distributed prosumer group, counts the clearing results of the distributed prosumer group and gives the transaction price curve within the distributed prosumer group. The energy operator, as the link between the energy supplier and the distributed prosumer group and as the follower of the energy supplier, will feedback the clearing results to the energy supplier when the distributed prosumer group is in short supply. The insufficient energy will be supplemented by the energy supplier. The energy supplier will determine the optimal output and formulate the electricity and heat prices to maximize the benefits of all market players.

[0058] 2. Energy supplier model

[0059] As the main energy producer of the integrated energy system, the energy supplier is responsible for the energy supply of most of the electricity and heat loads in the system. The energy supplier sells energy to energy operators by setting reasonable energy prices and obtains certain profits. The optimization goal is to maximize profits, which is expressed as follows:

[0060]

[0061] Where T is 24 hours, is the energy sales revenue to the energy operator at time t, is the capacity cost of the energy supplier at time t. The above items can be expressed as:

[0062]

[0063]

[0064]

[0065]

[0066] Where Δt represents the length of time, and are the electrical power and thermal power output by the energy supplier at time t, is the predicted wind power output at time t, are the wind power output power at time t, and are the output electric power and thermal power of the cogeneration unit at time t, and are the electrical power and thermal power output of the hydrogen fuel cell at time t, is the power consumption of the electrolytic cell at time t, is the power of the electric energy storage device at time t, and are the power purchased from the power grid and the power purchased from the gas grid by the energy supplier at time t, is the thermal power output of the gas boiler at time t, and are the electricity and heat prices sold by energy suppliers at time t, and are the electricity price of the power grid and the gas price of the gas grid at time t, is the carbon trading cost of the energy supplier, and γ is the wind abandonment penalty coefficient of the energy supplier.

[0067] The output of the cogeneration unit, gas boiler, hydrogen fuel cell and electrolyzer at time t must meet the following requirements:

[0068]

[0069]

[0070]

[0071]

[0072] In the formula They are the rated capacities of the cogeneration unit, gas boiler, hydrogen fuel cell and electrolyzer respectively.

[0073] The carbon trading costs of energy suppliers adopt a tiered carbon trading mechanism as follows:

[0074]

[0075] Where M d is the carbon emission rights trading amount of the comprehensive energy system, λ, l, α are the carbon trading base price, carbon emission range and price growth rate respectively. The above items can be expressed as:

[0076]

[0077]

[0078]

[0079] Where M buy,r ,M total,r is the actual carbon emissions of the power purchase and integrated energy system of the superior, MMR is the actual carbon emissions absorbed by the methane generator, M buy ,M CHP ,M GB are the carbon emission quotas of upper-level power purchase, cogeneration units and gas boilers, respectively; a1, b1, c1 and a2, b2, c2 are the carbon emission calculation parameters of coal-fired units and natural gas-consuming energy supply equipment, respectively.

[0080] 3. Energy operator model

[0081] Energy operators optimize the purchase and sale of electricity and heat prices based on the supply and demand relationship. They formulate pricing strategies based on the output plan on the energy supply side and the load demand on the energy consumption side. The optimization goal is to maximize the benefits, which is expressed as follows:

[0082]

[0083] In the formula is the energy sales revenue to the distributed prosumer group at time t, is the energy purchase cost from the energy supplier at time t, is the energy purchase cost from the distributed prosumer group at time t. The above items can be expressed as:

[0084]

[0085]

[0086]

[0087] In the formula, and are the electrical load and thermal load of the distributed prosumer group at time t, is the photovoltaic output power of the distributed prosumer group at time t, and are the electricity and heat prices sold by energy operators to distributed prosumers at time t, is the electricity purchase price from the distributed prosumer group at time t.

[0088] In order to avoid direct grid transactions between distributed prosumers, the energy operator’s energy purchase price should be slightly higher than the market price, and the energy selling price should be lower than the energy supplier’s energy selling price. The following constraints need to be met:

[0089]

[0090]

[0091] In the formula is the grid price at time t, are the lower and upper price limits of thermal energy at time t respectively.

[0092] 4. Distributed Prosumer Model

[0093] As a new type of market entity, distributed photovoltaic prosumers are both energy producers and energy consumers. When the photovoltaic output of distributed prosumers is not enough to meet their energy needs, they will purchase a certain amount of electricity from other distributed prosumers. If there is still a shortage, they will be supplemented by energy operators. Conversely, when the photovoltaic output of distributed prosumers is large, the excess electricity will be sold to other distributed prosumers. Since the cost of photovoltaic power generation is low, it has certain benefits. The optimization goal is to maximize consumer surplus, which is expressed as follows:

[0094]

[0095] In the formula is the utility function of distributed prosumer i at time t, expressed as follows:

[0096]

[0097] Where α e ,α h ,v e ,v h are the energy preference coefficients of distributed prosumers respectively.

[0098] In order to make distributed prosumers give priority to selling excess electricity to energy operators and maximize consumer surplus, the electricity price within the distributed prosumer group should meet the following constraints:

[0099]

[0100] 5. Energy sharing mechanism

[0101] The prosumer cluster first uses its own distributed photovoltaic resources for energy sharing. Distributed prosumers sell electricity directly to surrounding distributed prosumers, rather than selling it to the grid at a low price first and then buying it back from the grid at a high price. For example, residential prosumers have a small electricity load demand during the day and have surplus electricity, while industrial prosumers have a daytime electricity load demand that is much higher than their own photovoltaic supply and have insufficient electricity. Residential prosumers can sell surplus electricity directly to industrial prosumers. When the distributed photovoltaics within the prosumer group cannot meet the group's electricity load, they can then replenish electricity through the game transaction between energy operators and energy suppliers.

[0102] The high electricity costs for industry and commerce make distributed photovoltaic installations for self-generation and self-use more attractive, helping industrial and commercial prosumers reduce their electricity expenses. Larger industrial and commercial prosumers can also put surplus electricity online and obtain more power generation income. For residential prosumers, the amount of electricity generated by photovoltaic installations during energy sharing is no longer solely bound to themselves. Prosumers' choices will become more diverse, which can improve electricity stability and reduce electricity costs.

[0103] According to the above description of the integrated energy market architecture, Figure 2 As shown in the figure. The energy trading process between energy suppliers and energy operators, and between energy operators and distributed prosumers conforms to the dynamic game of master-slave hierarchy. They formulate energy trading strategies according to their own status and optimize the internal operation status. Energy operators act as leaders, and energy suppliers and distributed prosumers act as followers. The game optimization model is established using the idea of ​​master-slave game, which can be expressed as:

[0104]

[0105] Where EH, ES, and PS represent the three participants, namely, energy supplier, energy operator, and distributed prosumer group; S and F represent the strategy set and interest target set of each participant, respectively. To be the equilibrium solution of the master-slave game of the hydrogen-containing integrated energy system in this paper, it must satisfy:

[0106]

[0107] Where: They represent the optimal equilibrium solutions of the master-slave game among distributed prosumers, energy operators and energy suppliers respectively; S PS ,S ES ,S EH They represent the master-slave game strategy sets of distributed prosumer groups, energy operators and energy suppliers respectively.

[0108] In formula (24), it is impossible for energy suppliers, energy operators and distributed prosumers to obtain greater benefits by unilaterally changing their strategies. At this time, the equilibrium solution is the optimal strategy set of each participant in the master-slave game.

[0109] Proof: When the master-slave game model of the hydrogen-containing integrated energy system proposed in this paper meets the following conditions, it has a master-slave game equilibrium solution.

[0110] 1) The strategy sets of the leader and the follower are non-empty compact convex sets. According to the hydrogen-containing integrated energy system model, the leader's strategy needs to satisfy equations (18) and (19), the strategy of the follower on the energy supply side needs to satisfy equations (6)-(9), and the strategy of the follower on the load side needs to satisfy equation (22), so the strategy set of each participant is a compact subset in the metric space.

[0111] 2) Among them, the profit of energy supplier is F EH It is obtained from equations (1)-(5) and (10)-(113) that the profit of energy operators is ES It is obtained from equations (14)-(17), the profit F of distributed prosumers PS It is obtained from equations (20) and (21). Obviously, the three participants are continuous with respect to each decision variable;

[0112] 3) When the photovoltaic power generation of the distributed prosumer group is insufficient to meet its own load, the maximum consumer surplus of the distributed prosumer is expressed as follows:

[0113]

[0114] In the formula, we can see that except for the first term which is about Except for the convex function of is a linear function of It is also a convex function. Similarly, when the photovoltaic power generation of the distributed prosumer group meets its own load and has a surplus, is also a convex function, so About A quasi-convex function. In summary, the master-slave game model proposed in this paper has an equilibrium solution.

[0115] 6. Solution Algorithm

[0116] The master-slave game is a hierarchical game model. The leader first gives the strategy, and the follower then gives the best response based on the leader's strategy and passes the strategy to the leader. Due to the incompleteness of the strategy information, it takes multiple iterations to stabilize the game and reach the optimal value of the system. For the multi-agent master-slave game model established in this paper, the algorithm solution process is as follows: Figure 3 shown.

[0117] The algorithm solution process is as follows Figure 3 As shown in the figure, because the optimization goals of energy suppliers, energy operators and distributed prosumers are not consistent and the solutions also affect each other, a distributed optimization algorithm is used to solve the master-slave game model. In this algorithm, the upper layer uses a differential evolution algorithm to optimize the energy supplier's energy selling price, the energy operator's energy purchase is modeled using Yalmip and the CPLEX solver is called for solution, and the lower layer solves the optimal electricity consumption of the distributed prosumer group, thereby ensuring the maximum consumer surplus, speeding up the algorithm solution and ensuring the accuracy of the results.

[0118] If the optimal strategies obtained by each leader and follower in two consecutive times are the same, that is,

[0119]

[0120] The strategy combination is considered to have converged to the equilibrium point. At this point, no participant can change the strategy alone to gain more benefits without affecting other participants.

[0121] 7. Example Analysis

[0122] The optimization iterative process of energy suppliers and energy operators in the integrated energy system is as follows: Figure 4 It can be seen that the results converged at the 50th time, and the iterative benefits were balanced. When equilibrium is reached, the strategies of both parties no longer change, indicating that no participant can gain more benefits by changing their own strategies, indicating that the strategy at this time is the optimal solution for the game between the two parties. In the end, the energy supplier stabilized at 10,210 yuan, and the energy operator was 8,459 yuan.

[0123] The pricing strategy of the upper energy supplier is shown in Figure 5. In Figure 5(a), the lighter dashed line above and the darker dashed line below are the power grid's electricity sales price and on-grid electricity price, respectively. Energy suppliers formulate electricity price strategies within this envelope to provide energy operators with more attractive prices than the grid. Peaks appear at 13:00-15:00 and 23:00-24:00 in order to fully absorb renewable energy such as photovoltaic and wind power, reduce the amount of electricity purchased from the grid, and increase revenue. In Figure 5(b), the upper dashed line and the lower dashed line are the upper and lower limits of the heat price of the heat network, respectively. Similarly, formulating a heat price strategy within this envelope can also enable energy operators to purchase energy from energy suppliers first.

[0124] The price of electricity purchase and sale within the distributed prosumer group is as follows: Figure 6 As shown in the figure, the upper dotted line and the lower dotted line are the electricity sales price and the on-grid electricity price of the energy supplier respectively. Formulating the internal electricity price of the distributed prosumer group within this envelope can enable energy operators to purchase excess electricity from prosumers first, thereby reducing costs and greatly improving the on-site consumption of photovoltaic renewable energy.

[0125] The load changes of the distributed prosumer group before and after power sharing are as follows: Figure 7 As shown in the figure, the fluctuation range of the load curve of the prosumer becomes smaller. The peak of the electricity load shifts from 15:00-17:00 to 13:00-15:00. During this period, the sunlight is the strongest and the output photovoltaic power is the largest. Therefore, the maximum utilization rate of the photovoltaic device can be improved, maximizing the consumer surplus.

[0126] The internal energy transaction of distributed prosumers is shown in Figure 8. From the figure, we can see that the autonomy of mutual transactions among the distributed prosumers in the group of prosumers has increased. In addition to trading electricity with external operators, the prosumers in the group of prosumers will first autonomously share energy.

[0127] It can be seen from Figure 8(a), Figure 8(b) and Figure 8(c) that both commercial and residential prosumers have surplus photovoltaic power, so energy sharing is prioritized within the distributed prosumer group. If there is any surplus, it will be recycled by the energy operator.

[0128] In Figure 8(d), the industrial prosumer’s own photovoltaic power generation cannot meet the load demand, so it purchases electricity from the distributed prosumer group. If the power is still insufficient, the energy operator will supply it, thereby reducing the cost of purchasing electricity and increasing consumer surplus.

[0129] Table 1 Gains after energy sharing between source operators and multiple types of distributed prosumers

[0130]

[0131] As shown in Table 1, after energy sharing, the benefits of energy operators and different types of prosumers have increased compared to before. The energy operator's daily gain is finally 875 yuan, while the commercial prosumer's daily gain is about 200 yuan, the residential prosumer's daily gain is 84 yuan, and the industrial prosumer's daily gain is 447 yuan. The results show that the model in this paper has improved the benefits of each subject in the integrated energy system, and the degree of benefit increase varies depending on the role played. This paper assumes that there are only four prosumers in the distributed prosumer group in the integrated energy system, but the actual system requires a large number of prosumers, so the total gain of the system will be greater.

[0132] The results of the energy supplier's electricity, heat, gas and hydrogen optimization scheduling are shown in Figure 9. In Figure 9(a), considering the improvement of the utilization rate of renewable energy, photovoltaic and wind power output will be sold to energy operators first, and the excess renewable energy will be stored by electrolyzers and electric energy storage devices. Gas turbines and hydrogen fuel cells are used as supplements to make up for the lack of renewable energy, thereby meeting the load requirements of energy operators. Wind power is abundant at night, so hydrogen fuel cells are used to generate heat to achieve full consumption of wind power. When the heat generated by hydrogen fuel cells and gas turbines is not enough to supply the heat load of distributed prosumers, gas boilers are used to supply heat. By adjusting the heat selling price of energy suppliers, the output of gas boilers is guided to achieve a balance between supply and demand, as shown in Figure 9(b). In order to fully consume renewable energy such as wind power, the electrolyzer uses renewable energy to produce hydrogen, which is converted into natural gas by a methane generator and supplied to gas turbines and gas boilers, reducing part of the cost of purchasing gas from the gas grid and achieving economic operation of the system, as shown in Figure 9(c). In Figure 9(d), when the output of renewable energy is large, the hydrogen produced by the electrolyzer consuming renewable energy is not only supplied to the methane generator and hydrogen fuel cell, but the remaining hydrogen is also stored in the hydrogen energy storage device. When the load demand is too large, it is released from the hydrogen energy storage device and supplied to the hydrogen fuel cell to generate electricity and heat, achieving a balance between supply and demand and realizing clean and economical operation of the system.

[0133] In addition, we compared the low-carbon economic operation results of energy suppliers considering tiered carbon trading with the traditional economic operation without tiered carbon trading. The results are as follows:

[0134] Table 2 Carbon emissions and operating costs of energy suppliers

[0135] parameter Considering carbon trading ladder No consideration of carbon trading ladder Carbon emissions / kg 7192 7266 Carbon trading cost / yuan 2396 3633 Energy purchase cost / yuan 11776 11752 Total cost / yuan 14472 15385

[0136] As can be seen from Table 2, the introduction of tiered carbon trading among energy suppliers and the consideration of the environmental impact of carbon emissions from output equipment can reduce carbon emissions by about 74 kg, effectively achieving carbon emission reduction and making the integrated energy system more environmentally friendly. At the same time, the total operating cost is reduced from 15,385 yuan to 14,472 yuan, achieving low-carbon economic operation of the integrated energy system.

[0137] 8. This paper proposes a multi-agent operation game strategy for a multi-agent hydrogen-containing integrated energy system considering energy sharing. The distributed prosumer group is regarded as a follower, and the energy supplier and energy operator are regarded as a leader. The equilibrium interaction strategy is solved to achieve multi-agent collaborative optimization operation.

[0138] The effectiveness of the operation strategy is verified through case analysis, and the following conclusions are obtained:

[0139] 1) Considering the step-by-step carbon trading mechanism in the optimization operation of hydrogen-containing integrated energy systems can effectively achieve carbon emission reduction and ensure the environmental friendliness of system operation.

[0140] 2) Distributed prosumers give full play to their autonomy, flexibly choose trading methods, and adjust load demand response under the time-of-use energy price mechanism, so that they can use energy flexibly and maximize surplus while meeting comfort requirements, thereby promoting the local consumption of renewable energy.

[0141] 3) By introducing multi-agent energy management strategies and transaction operation strategies based on master-slave game optimization on both the supply and demand sides, the benefits of each subject in the integrated energy system can be increased, mutual benefit and win-win results can be achieved for each subject, and the overall economic effect of the system can be more obvious.

[0142] Embodiment 2

[0143] This embodiment provides a multi-agent integrated energy system optimization operation system considering energy sharing, including:

[0144] The integrated energy system building module is configured to analyze the mutual coupling mechanism of electricity, heat, gas and hydrogen and introduce a step-by-step carbon trading mechanism to build an integrated energy system;

[0145] A master-slave game model building module is configured to build a master-slave game model on both the supply and demand sides based on the mathematical models of the integrated energy system supplier, the integrated energy operator and the distributed prosumer;

[0146] The model solving module is configured to use a two-layer optimization algorithm to solve the master-slave game model on both the supply and demand sides to obtain the optimal operation strategy of the integrated energy system.

[0147] The examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the contents disclosed in the above embodiment 1. It should be noted that the above modules as part of the system can be executed in a computer system such as a set of computer executable instructions.

[0148] The description of each embodiment in the above embodiments has different emphases. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0149] The proposed system can be implemented in other ways. For example, the system embodiment described above is only illustrative, and the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0150] Embodiment 3

[0151] 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 method for optimizing the operation of a multi-agent integrated energy system considering energy sharing as described in the first embodiment above are implemented.

[0152] Embodiment 4

[0153] 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 method for optimizing the operation of a multi-agent integrated energy system considering energy sharing as described in the first embodiment above are implemented.

[0154] 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.

[0155] 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.

[0156] 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 including 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.

[0157] 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 in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0158] 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.

[0159] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A multi-agent integrated energy system optimization operation method considering energy sharing, characterized in that: include: Analyze the mutual coupling mechanism of electricity, heat, gas and hydrogen and introduce a step-by-step carbon trading mechanism to build a comprehensive energy system; Based on the mathematical models of energy suppliers, energy operators and distributed prosumers, a master-slave game model on both the supply and demand sides is established; A two-layer optimization algorithm is used to solve the master-slave game model on both the supply and demand sides to obtain the optimal operation strategy of the integrated energy system; The mathematical model of the energy supplier has the optimization goal of maximizing revenue, specifically: Where T is 24 hours, is the energy sales revenue to the energy operator at time t, is the capacity cost of the energy supplier at time t; the above items are expressed as: In the formula, Δt represents the length of time, and are the electrical power and thermal power output by the energy supplier at time t, is the predicted wind power output at time t, are the wind power output power at time t, and are the output electric power and thermal power of the cogeneration unit at time t, and are the electrical power and thermal power output of the hydrogen fuel cell at time t, is the power consumption of the electrolytic cell at time t, is the power of the electric energy storage device at time t, and are the power purchased from the power grid and the power purchased from the gas grid by the energy supplier at time t, is the thermal power output of the gas boiler at time t, and are the electricity and heat prices sold by energy suppliers at time t, and are the electricity price of the power grid and the gas price of the gas grid at time t, is the carbon trading cost of the energy supplier, γ is the wind abandonment penalty coefficient of the energy supplier; The mathematical model of the distributed prosumer has the optimization goal of maximizing consumer surplus, specifically: In the formula, is the utility function of distributed prosumer i at time t, expressed as follows: In the formula, α e ,α h ,v e ,v h are the energy preference coefficients of distributed prosumers; In order to make distributed prosumers give priority to selling excess electricity to energy operators and maximize consumer surplus, the electricity price within the distributed prosumer group should meet the following constraints: In the formula, is the grid price at time t, is the electricity purchase price from distributed prosumers at time t, is the electricity price sold by the energy supplier at time t; The master-slave game model on both the supply and demand sides is specifically: In the formula, EH, ES, and PS represent the three participants, namely, energy supplier, energy operator, and distributed prosumer group; S and F represent the strategy set and interest target set of each participant, respectively; if To be the equilibrium solution of the master-slave game of the hydrogen-containing integrated energy system in this paper, it must satisfy: In the formula, energy suppliers, energy operators and distributed prosumers cannot obtain greater benefits by unilaterally changing strategies. At this time, the equilibrium solution is the optimal strategy set of each participant in the master-slave game; The two-layer optimization algorithm is used to solve the master-slave game model on both the supply and demand sides to obtain the optimal operation strategy of the integrated energy system, including: To solve the master-slave game model on both the supply and demand sides, the upper layer uses the differential evolution algorithm to optimize the energy supplier's energy selling price. The energy operator's energy purchase capacity is modeled using Yalmip and solved by calling the CPLEX solver. The lower layer solves the optimal electricity consumption of the distributed prosumer group to ensure the maximum consumer surplus. If the optimal strategies obtained by each leader and follower in two consecutive times are the same, It is considered that the strategy combination has converged to the equilibrium point; at this time, no participant can change the strategy alone to obtain more benefits without affecting other participants.

2. The multi-agent integrated energy system optimization operation method considering energy sharing as claimed in claim 1 is characterized in that: The comprehensive energy system includes four parts: energy supply side, multi-energy coupling link, energy storage link and demand side; The energy supply side consists of power grid, gas grid, centralized wind power and photovoltaic devices. The equipment in the multi-energy coupling link consists of electrolyzer, methane generator, hydrogen fuel cell, gas turbine and gas boiler. The energy storage link includes electric energy storage and hydrogen energy storage. The demand side consists of residential, commercial and industrial prosumers with different capacities. Among them, the electric load is supplied by its own photovoltaic equipment, cogeneration units, hydrogen fuel cells and wind, solar and storage equipment, and the shortfall is supplemented by the external power grid. The thermal load is supplied by cogeneration units and gas boilers, the gas load is supplied by methane generators and external gas networks, and the hydrogen load is supplied by electrolyzers and hydrogen storage equipment.

3. The multi-agent integrated energy system optimization operation method considering energy sharing as claimed in claim 1, characterized in that: In the mathematical model of the energy supplier, the output of the cogeneration unit, gas boiler, hydrogen fuel cell and electrolyzer at time t must meet the following requirements: In the formula, They are the rated capacities of the cogeneration unit, gas boiler, hydrogen fuel cell and electrolyser; The carbon trading costs of energy suppliers adopt a tiered carbon trading mechanism as follows: Where M d is the carbon emission rights trading amount of the comprehensive energy system, λ, l, α are the carbon trading base price, carbon emission range and price growth rate respectively; the above items are expressed as: M d =M buy,r +M total,r -M MR -M buy -M CHP -M GB Where M buy,r ,M total,r is the actual carbon emissions of the power purchase and integrated energy system of the superior, M MR is the actual carbon emissions absorbed by the methane generator, M buy ,M CHP ,M GB are the carbon emission quotas of upper-level power purchase, cogeneration units and gas boilers, respectively; a1, b1, c1 and a2, b2, c2 are the carbon emission calculation parameters of coal-fired units and natural gas-consuming energy supply equipment, respectively.

4. The multi-agent integrated energy system optimization operation method considering energy sharing as claimed in claim 1, characterized in that: The mathematical model of the energy operator has the optimization goal of maximizing the profit, which is as follows: In the formula, is the energy sales revenue to the distributed prosumer group at time t, is the energy purchase cost from the energy supplier at time t, is the energy purchase cost from the distributed prosumer group at time t; the above items are expressed as: In the formula, and are the electrical load and thermal load of the distributed prosumer group at time t, is the photovoltaic output power of the distributed prosumer group at time t, and are the electricity and heat prices sold by energy operators to distributed prosumers at time t, is the electricity purchase price from the distributed prosumer group at time t; In order to avoid direct grid transactions between distributed prosumers, the following constraints must be met: In the formula, is the grid price at time t, are the lower and upper price limits of thermal energy at time t respectively.

5. A multi-agent integrated energy system optimization operation system considering energy sharing, characterized in that: include: The integrated energy system building module is configured to analyze the mutual coupling mechanism of electricity, heat, gas and hydrogen and introduce a step-by-step carbon trading mechanism to build an integrated energy system; A master-slave game model building module is configured to build a master-slave game model on both the supply and demand sides based on the mathematical models of energy suppliers, energy operators and distributed prosumers; The mathematical model of the energy supplier has the optimization goal of maximizing revenue, specifically: Where T is 24 hours, is the energy sales revenue to the energy operator at time t, is the capacity cost of the energy supplier at time t; the above items are expressed as: In the formula, Δt represents the length of time, and are the electrical power and thermal power output by the energy supplier at time t, is the predicted wind power output at time t, are the wind power output power at time t, and are the output electric power and thermal power of the cogeneration unit at time t, and are the electrical power and thermal power output of the hydrogen fuel cell at time t, is the power consumption of the electrolytic cell at time t, is the power of the electric energy storage device at time t, and are the power purchased from the power grid and the power purchased from the gas grid by the energy supplier at time t, is the thermal power output of the gas boiler at time t, and are the electricity and heat prices sold by energy suppliers at time t, and are the electricity price of the power grid and the gas price of the gas grid at time t, is the carbon trading cost of the energy supplier, γ is the wind abandonment penalty coefficient of the energy supplier; The mathematical model of the distributed prosumer has the optimization goal of maximizing consumer surplus, specifically: In the formula, is the utility function of distributed prosumer i at time t, expressed as follows: In the formula, α e ,α h ,v e ,v h are the energy preference coefficients of distributed prosumers; In order to make distributed prosumers give priority to selling excess electricity to energy operators and maximize consumer surplus, the electricity price within the distributed prosumer group should meet the following constraints: In the formula, is the grid price at time t, is the electricity purchase price from distributed prosumers at time t, is the electricity price sold by the energy supplier at time t; The master-slave game model on both the supply and demand sides is specifically: In the formula, EH, ES, and PS represent the three participants, namely, energy supplier, energy operator, and distributed prosumer group; S and F represent the strategy set and interest target set of each participant, respectively; if To be the equilibrium solution of the master-slave game of the hydrogen-containing integrated energy system in this paper, it must satisfy: In the formula, energy suppliers, energy operators and distributed prosumers cannot obtain greater benefits by unilaterally changing strategies. At this time, the equilibrium solution is the optimal strategy set of each participant in the master-slave game; The model solving module is configured to solve the master-slave game model on both the supply and demand sides using a two-layer optimization algorithm to obtain the optimal operation strategy of the integrated energy system; The two-layer optimization algorithm is used to solve the master-slave game model on both the supply and demand sides to obtain the optimal operation strategy of the integrated energy system, including: To solve the master-slave game model on both the supply and demand sides, the upper layer uses the differential evolution algorithm to optimize the energy supplier's energy selling price. The energy operator's energy purchase capacity is modeled using Yalmip and solved by calling the CPLEX solver. The lower layer solves the optimal electricity consumption of the distributed prosumer group to ensure the maximum consumer surplus. If the optimal strategies obtained by each leader and follower in two consecutive times are the same, It is considered that the strategy combination has converged to the equilibrium point; at this time, no participant can change the strategy alone to obtain more benefits without affecting other participants.

6. 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 method for optimizing operation of a multi-agent integrated energy system considering energy sharing as described in any one of claims 1 to 4 are implemented.

7. 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 method for optimizing operation of a multi-agent integrated energy system considering energy sharing as described in any one of claims 1 to 4 are implemented.

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