A collaborative optimization operation method for multi-energy alliance considering power generation rights sharing

Abstract

The present invention discloses a multi-energy alliance collaborative optimization operation method considering the sharing of power generation rights, which belongs to the field of multi-energy collaborative power generation operation and includes the following steps: S1. Establishing a multi-energy alliance with information sharing; S2. Determining the total output of the multi-energy alliance based on the clearing results; S3. Setting research assumptions; S4. Constructing a multi-energy collaborative optimization model considering the electricity-carbon-green certificate market trading mechanism; S5. Based on the multi-energy collaborative optimization model, under the condition that the total output of the multi-energy alliance remains unchanged, matching the carbon quota sharing of each power generation entity; S6. Determining the declared output plan of the multi-energy alliance; S7. Clearing according to the declared output plan of the multi-energy alliance. The above-mentioned multi-energy alliance collaborative optimization operation method considering the sharing of power generation rights is adopted to participate in the clearing of the electricity spot market in the form of an alliance, and optimize the internal resource allocation of the alliance through power generation rights sharing and green certificate trading, thereby improving economic benefits and new energy absorption capacity.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-energy collaborative power generation operation, and in particular to a multi-energy alliance collaborative optimization operation method considering power generation right sharing. Background Art

[0002] In recent years, with the advancement of global energy transformation and low-carbon development, multi-energy complementarity and electricity-carbon trading mechanisms have become important means to improve energy system efficiency and promote renewable energy integration. However, in actual operation, they still face many challenges, such as the coupling and coordination of multiple energy systems and the optimization of market mechanisms. In-depth research on the coordinated optimization of multi-energy complementarity systems and carbon market mechanisms is of great significance for achieving low-carbon and efficient energy system operation.

[0003] At present, relevant research at home and abroad mainly focuses on the following two aspects:

[0004] First, focusing on the diverse energy needs of end users and combining the complementary characteristics of traditional and new energy sources, we can optimize the energy supply and demand balance structure to achieve efficient resource allocation. For example, we can achieve multi-energy coordinated supply and cascaded energy utilization through multi-level combined supply and supply of natural gas, heat, electricity, and cooling, or energy integration systems. For example, Li Peng, Wang Zixuan, Wang Jiahao, et al. proposed a multi-time-space scale energy supply and demand balance method based on day-ahead, intraday, and real-time levels in "A multi-time-space scale optimal operation strategy for a distributed integrated energy system." Lu Qing, Guo Qisheng, and Zeng Wei published an analysis of community users' multi-energy demand response in "Optimization scheduling of integrated energy service system in community: A bi-layer optimization model considering multi-energy demand response and user satisfaction," and Wang Xingang, Zhao Fang, and Zhu Wenjun published an analysis of community users' multi-energy demand response in "Analysis of regional energy consumption characteristics based on integrated energy metering data." By optimizing user energy consumption behavior, they improved the economic efficiency of integrated energy use and user satisfaction. In their paper "A Station-Grid-Load Collaborative Planning Method for Integrated Energy Systems Considering Flexible Load Allocation," Liu Hang, Zhao Haipeng, Wenming, et al., and in their paper "A Study on Optimal Operational Strategies for Integrated Park Energy Systems Considering Coupled Response Characteristics of Cooling, Heating, and Electricity Demand," Zhao Haipeng, Miao Shihong, Li Chao, et al. constructed collaborative optimization planning models for integrated energy or source-grid-load models. Case studies demonstrated that the proposed methods can effectively reduce system investment and transmission energy consumption. Existing research on economic optimization scheduling for multi-energy complementary systems primarily focuses on model construction, optimal power generation combinations for various entities, and compensation mechanisms for each entity's benefits.

[0005] It can be seen that the above research has achieved results in reducing system operating costs and promoting clean energy consumption, but the research on how to incentivize long-term collaboration and benefit sharing mechanisms among various entities is not in-depth enough.

[0006] The second area concerns the design and coordinated coupling of multi-level market mechanisms. For example, Li Bingyang, Li Xinli, Yang Guotian, et al., in "Optimal Dispatching of Joint Wind, Photovoltaic, Hydropower, and Storage Power Supply Systems for Thermal Power Plants," and Li X, Tan Z, Shen J, et al., in "Research on the Operation Strategy of Joint Wind-Photovoltaic-Hydropower-Pumped Storage Participation in the Electricity Market Based on Nash Negotiation," further explored the joint optimization of multi-energy generation systems in the electricity market and ancillary service markets. In "Low-Carbon Dispatching Technology for Wind, Photovoltaic, Hydropower, and Storage Systems Based on Equilibrium Costs of Carbon Trading and Carbon Capture," Wang Rongmao, Liu Miao, Zhang Ye, et al., constructed a multi-energy system dispatch model under a single carbon price and explored the guiding role of carbon trading mechanisms in carbon emission reduction. However, existing research has found that a single carbon price mechanism helps to establish a carbon market framework, but it cannot effectively reflect market supply and demand dynamics. The tiered carbon price mechanism can make up for the shortcomings of the single carbon price mechanism. That is, by setting different carbon quota price mechanisms, it can effectively incentivize high-carbon emission enterprises to implement energy conservation and emission reduction. This mechanism can also reflect the real-time supply and demand relationship of market trading entities.

[0007] In their paper "Transitional Mechanism for Renewable Energy to Participate in the Spot Market under Green Certificate Trading," Wang Kun, Xu Chengwei, and Wen Fushuan proposed a strategy for new energy power generation companies to participate in the electricity spot market within the green certificate trading system. Through simulation experiments, they verified the feasibility of this strategy in increasing the revenue of new energy power generators, providing a new approach for reducing their reliance on government subsidies. In their paper "Economic and Low-Carbon Dispatch Strategies for Interregional Interconnected Systems to Increase Green Certificate Demand," Zhang Hong, Meng Qingyao, Ma Hongjun, et al. further studied economic and low-carbon dispatch strategies for power systems that consider green certificate demand and proposed a green certificate pricing mechanism based on supply and demand. In their paper "New Energy Interprovincial Trading Model Considering Carbon-Green Certificate Trading Mechanism," Liu Kezhen, Dai Yinghao, Zhao Qingli, et al. established an interprovincial trading model for new energy operators that comprehensively considers both carbon trading and green certificate trading. By introducing these two mechanisms, they significantly reduced the curtailment rates of wind and photovoltaic power.

[0008] It can be seen that although the above research has promoted the development of the electricity-carbon-green certificate mechanism, system carbon emission reduction and economic efficiency improvement, there is a lack of in-depth discussion on the synergistic effect of the electricity-carbon-green certificate market mechanism. At the same time, the internal coordinated low-carbon optimization problem of multi-energy complementary systems participating in the multi-level market mechanism needs further research. Summary of the Invention

[0009] The purpose of the present invention is to provide a multi-energy alliance collaborative optimization operation method considering the sharing of power generation rights to solve the above technical problems.

[0010] To achieve the above objectives, the present invention provides a multi-energy alliance collaborative optimization operation method considering power generation right sharing, comprising the following steps:

[0011] S1. Identify various types of power generation entities participating in power spot market transactions, and establish a multi-energy alliance for information sharing based on the identified various types of power generation entities;

[0012] S2. Determine the total output of the multi-energy alliance based on the clearing results of the electricity spot market transactions described in step S1 to ensure that the total output of the multi-energy alliance is consistent with market demand;

[0013] S3. Set research assumptions, including reasonable distribution of benefits, free electricity trading, participation of the multi-energy alliance as a whole in electricity spot market transactions, and a virtual sharing model for power generation rights sharing under the dual influence of market trading rules and physical constraints of each power generation entity;

[0014] S4. Under the research assumptions set in step S3, a power generation rights sharing mechanism is introduced, and with the goal of maximizing the benefits of the multi-energy alliance, a multi-energy collaborative optimization model considering the electricity-carbon-green certificate market trading mechanism is constructed;

[0015] S5. Based on the multi-energy collaborative optimization model described in step S4, and under the condition that the total output of the multi-energy alliance described in step S2 remains unchanged, performing carbon quota sharing matching for each power generation entity;

[0016] S6. Determine the output plan declared by the multi-energy alliance based on the carbon quota sharing matching result determined in step S5;

[0017] S7. The electricity spot market is cleared according to the output plan declared by the multi-energy alliance determined in step S6.

[0018] Preferably, in step S1, the multiple types of power generation entities include wind power, thermal power, hydropower and photovoltaic power generation, and information is shared among wind power, thermal power, hydropower and photovoltaic power generation;

[0019] In step S2, the dispatch center monitors the clearing results.

[0020] Preferably, the rationality of the benefit distribution in step S3 includes individual rationality, alliance rationality, and group rationality, wherein individual rationality means that the benefits distributed to the power generation entity within the multi-energy alliance are not less than the benefits it can obtain by operating independently; alliance rationality means that the benefits distributed to the power generation entity in the current multi-energy alliance are not less than the benefits distributed in other multi-energy alliances; and group rationality means that the benefit distribution of all power generation entities in the multi-energy alliance satisfies the distribution optimization equilibrium.

[0021] Freedom of electricity trading means that power generation entities within the multi-energy alliance can freely trade electricity to meet the balance of power supply and demand.

[0022] Preferably, the matching objective function of the power generation rights sharing mechanism described in step S4 is as follows:

[0023]

[0024] P ij ≥0 (2);

[0025] Where, P ij is the power generation right shared by the sharing party and the receiving party, α gre is the transaction price of the green certificate market;

[0026] The set of power generation entities participating in sharing φ={N sha ,N rec}, where N sha N is the party sharing the power generation rights, rec The power generation right receiver is the power generation right receiver. The matching type is determined according to the types of the power generation right sharer and the power generation right receiver. When the power generation right sharer is thermal power and the power generation right receiver is renewable energy, the renewable energy is wind power or photovoltaic power generation, and the renewable energy is consumed first. The matching set is as follows:

[0027] N sha ={P car,w ,α car,w ,F car,w} (3);

[0028] N rec ={P gre,v ,α gre,v ,F gre,v} (4);

[0029] Where, P car,w The shareable amount of power generation rights declared by the sharing party, α car,w is the electricity price corresponding to the shareable amount of power generation rights, F car,w P is the income obtained by the power generation right sharing party after receiving it. gre,v is the power generation right declared by the power generation right recipient, α gre,v F is the green certificate price corresponding to the power generation right received by the power generation right receiver, gre,v The green certificate income obtained by the power generation right recipient after receiving the power generation right;

[0030] When the power generation rights sharing party is thermal power and the power generation rights receiving party is hydropower, the matching set is as follows:

[0031] N rec ={P pum,v ,α pum,v ,F pum,v} (5);

[0032] Where, P pum,v is the power generation right declared by the power generation right recipient, α pum,v is the electricity price corresponding to the power generation right received by the power generation right receiver, F pum,v The income from increased power generation obtained by the recipient of power generation rights after receiving the power generation rights;

[0033] The sharing matching rules of the power generation rights sharing mechanism include: (1) maximizing demand matching: in each power generation rights sharing matching process, the sharing party and the receiving party prioritize reaching the maximum transaction amount; (2) all thermal power generation rights are transferred, and only the power generation corresponding to the minimum output is retained to ensure the basic operation needs of the system; (3) assuming that the daytime period is 7:00-19:00, the order of receiving daytime power generation rights is: photovoltaic power, wind power, hydropower; assuming that the nighttime period is 20:00-6:00 the next day, the order of receiving nighttime power generation rights is adjusted to: wind power, hydropower;

[0034] The objective function expression for maximizing the benefits of the multi-energy alliance is as follows:

[0035] max F all =F ele +F car +F gre (6);

[0036] Where, F all is the total benefit of the multi-energy alliance, F ele 、F car and F gre These are the profits of the multi-energy alliance from participating in the electricity spot market, carbon emission rights market and green certificate market respectively;

[0037] in,

[0038]

[0039] F car =α car R car (8);

[0040] F gre =α gre P gre (9);

[0041] Where, is the clearing settlement price of the multi-energy alliance in the electricity spot market during period t, is the cleared electricity quantity of the multi-energy alliance in the electricity spot market during period t, α car and R car are the transaction price of carbon emission rights market and the number of carbon quotas sold by multi-energy alliances, P greThe number of green certificates traded within the Multi-Energy Alliance;

[0042] The power clearing model expression is as follows:

[0043]

[0044] In the formula, m represents the total number of power generation entities, a i,bid represents the bid of the i-th power generation entity, a L,bid is the unit cost of electricity purchase, P i t and They represent the clearing power and load value of power generation entity i during period t respectively;

[0045] The constraints of the multi-energy collaborative optimization model include power balance constraints, upper and lower power limits of each power generation entity, start and stop constraints of each power generation entity, and ramp constraints of each power generation entity. The power balance constraint expression is as follows:

[0046]

[0047] The upper and lower power constraint expressions of each power generation entity are as follows:

[0048]

[0049] Where, P i min and P i max They represent the lower and upper limits of the power clearing of power generation entity i during period t respectively;

[0050] The start-stop constraint expressions of each power generation entity are as follows:

[0051]

[0052] Where, P i represents the clearing power of power generation entity i, x i represents the start and stop status of the power generation entity i, and x i =0,1, when x i =1 means start, when x i =0 means stop;

[0053] The ramp constraint expressions of each power generation entity are as follows:

[0054]

[0055] Where, P i t-1 represents the clearing power of power generation entity i in period t-1, P i up and P i downThey represent the upper and lower limits of the ramp rate of power generation entity i respectively;

[0056] The solution method of the multi-energy collaborative optimization model is to first reformulate the KKT optimal conditions and then solve it using the big M method.

[0057] Preferably, the transaction price of carbon emission rights market is α car The expression is as follows:

[0058]

[0059] Where, is the carbon trading benchmark price, d is the carbon trading range; δ is the carbon price growth rate.

[0060] Preferably, the Big M method solution steps are as follows:

[0061] The first step is to reformulate the multi-energy collaborative optimization model using the KKT optimal conditions: According to the KKT conditions, the power clearing model is integrated into the constraint conditions, and the Lagrangian multiplier is introduced to combine the constraint conditions shown in formulas (10) to (14) with the objective function shown in formula (6) to form the Lagrangian function:

[0062]

[0063] Where λ b is the Lagrange multiplier for the power balance constraint; and are the Lagrange multipliers of the upper and lower power constraints of power generation entity i, and is the Lagrange multiplier for the lower and upper limits of the ramp rate of power generation entity i;

[0064] The second step is to linearize the reformulated multi-energy collaborative optimization model using the Big M method and solve the linear programming algorithm. The decision variables of the power clearing model are the quotation of participating in the power spot market. The output values ​​of wind power, photovoltaic power generation, hydropower and thermal power are The decision variable of the power generation rights sharing model is R car and P ij .

[0065] Preferably, step S5 specifically includes the following steps:

[0066] S51. Obtaining carbon dioxide emissions from each power generation entity;

[0067] S52. Calculate the remaining quota based on the carbon emission standards;

[0068] S53. Implement quota exchange: Based on the power generation rights sharing mechanism, power generation entities with surplus quotas are allowed to sell them to power generation entities that lack quotas.

[0069] Therefore, the present invention adopts the above-mentioned multi-energy alliance collaborative optimization operation method considering the sharing of power generation rights, which has the following beneficial effects:

[0070] 1. Improve economic benefits: Through a reasonable profit distribution mechanism and power generation rights sharing mechanism, the efficiency of capital circulation within the alliance is improved, promoting win-win cooperation among all parties;

[0071] 2. Promote the development of clean energy: Special emphasis is placed on the principle of prioritizing the consumption of new energy, which will help accelerate the development of clean energy;

[0072] 3. Addressing climate change: Through the sharing and matching of carbon quotas and the introduction of a carbon emission rights market, we have directly supported carbon emission reduction targets and made contributions to addressing climate change;

[0073] 4. Enhance system flexibility: Allowing the flexible adjustment of the order of receiving power generation rights (photovoltaic-wind power-hydropower-thermal power), increasing the adaptability and response speed of the system, and enabling quick response in the face of emergencies.

[0074] In summary, this paper explores the sharing mechanism of power generation rights within a multi-energy alliance by constructing a power generation rights sharing platform to promote carbon emission reduction, optimize economic benefits, and enhance the absorption capacity of new energy. Furthermore, it analyzes the synergistic effects between heterogeneous energy sources such as wind power, photovoltaic power, hydropower, and thermal power, and their impact on the overall optimization objectives of the system, and verifies the supporting role of the power generation rights sharing mechanism in the optimization process. This paper aims to lay the foundation for achieving the optimal power generation portfolio, high-yield operation, and efficiency improvement of the multi-energy alliance, and to provide a decision-making method for the multi-energy alliance to participate in the electricity-carbon-green certificate market.

[0075] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 This is a flowchart of a multi-energy alliance collaborative optimization operation method considering power generation right sharing according to the present invention;

[0077] Figure 2 The load curve of each node of the IEEE14-node system described in the simulation experiment;

[0078] Figure 3 Output curves of wind power plants and photovoltaic power stations described in the simulation experiment;

[0079] Figure 4 This is a comparison chart of the clearing electricity prices of Scheme 1 and Scheme 2 described in the simulation experiment;

[0080] Figure 5 The following are the comparison results of the multi-market benefits described in the simulation experiment, where (a) is the total benefit comparison result diagram between Scheme 1 and Scheme 2, (b) is the electricity benefit comparison result diagram of each power generation entity, (c) is the carbon benefit and green certificate benefit comparison result diagram of each power generation entity, (d) is the contribution comparison result diagram of each power generation entity in participating in the multi-market, (e) is the carbon quota trading volume comparison result diagram between Scheme 1 and Scheme 2, and (f) is the green certificate trading volume comparison result diagram between Scheme 1 and Scheme 2;

[0081] Figure 6 The output result diagrams of Scheme 1 and Scheme 2 described in the simulation experiment, where (a) is the output result diagram of each power generation entity of Scheme 1, and (b) is the output result diagram of each power generation entity of Scheme 2;

[0082] Figure 7 This is a comparison chart of carbon emissions and new energy consumption for Scheme 1 and Scheme 2 described in the simulation experiment. DETAILED DESCRIPTION

[0083] In order to make the purposes, technical solutions and advantages disclosed in the embodiments of the present invention clearer, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present invention and are not intended to limit the embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. Examples of the embodiments are shown in the accompanying drawings, where the same or similar numbers throughout represent the same or similar elements or elements with the same or similar functions.

[0084] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products or devices.

[0085] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0086] like Figure 1 As shown, a multi-energy alliance collaborative optimization operation method considering power generation right sharing includes the following steps:

[0087] S1. Identify various types of power generation entities participating in power spot market transactions, and establish a multi-energy alliance for information sharing based on the identified various types of power generation entities;

[0088] In step S1, multiple types of power generation entities include wind power, thermal power, hydropower and photovoltaic power generation, and information is shared among wind power, thermal power, hydropower and photovoltaic power generation;

[0089] S2. Determine the total output of the multi-energy alliance based on the clearing results of the electricity spot market transactions described in step S1 to ensure that the total output of the multi-energy alliance is consistent with market demand;

[0090] In step S2, the dispatch center monitors the clearing results.

[0091] S3. Establish research assumptions, including reasonable distribution of benefits, free electricity trading, participation of the multi-energy alliance as a whole in electricity spot market transactions, and the adoption of a virtual sharing model for the power generation rights sharing mechanism under the dual influence of market trading rules and the physical constraints of each power generation entity. The virtual sharing model can effectively achieve information sharing, shared matching of power generation rights, and optimized decision-making functions, supporting the flexible and collaborative operation of the multi-energy alliance in diversified market transactions;

[0092] Assuming that there are various types of cooperation among the four energy sources of wind power, photovoltaic power generation, hydropower and thermal power, and each entity expects to gain benefits under different combinations, in order to achieve the stability of the alliance operation, the reasonable distribution of benefits described in step S3 includes individual rationality, alliance rationality and group rationality. Among them, individual rationality means that the benefits distributed to the power generation entity in the multi-energy alliance are not less than the benefits it can obtain by operating independently; alliance rationality means that the benefits distributed to the power generation entity in the current multi-energy alliance are not less than the benefits distributed in other multi-energy alliances (otherwise it will tend to withdraw from the current multi-energy alliance); group rationality means that the benefit distribution of all power generation entities in the multi-energy alliance meets the distribution optimization equilibrium to maintain the overall stability of the alliance;

[0093] Freedom of electricity trading means that power generation entities within the multi-energy alliance can freely trade electricity to meet the balance of power supply and demand.

[0094] S4. Under the research assumptions set in step S3, a power generation rights sharing mechanism is introduced, and with the goal of maximizing the benefits of the multi-energy alliance, a multi-energy collaborative optimization model considering the electricity-carbon-green certificate market trading mechanism is constructed;

[0095] Since only thermal power plants have carbon quotas within the multi-energy alliance, it is assumed that the power generation rights of thermal power plants are shared within the alliance. That is, thermal power plants can save carbon quotas by reducing their output, or share power generation rights with wind power or photovoltaic power generation to reduce system carbon emissions. At the same time, the remaining carbon quotas can be traded in the carbon market. Therefore, the matching objective function of the power generation rights sharing mechanism described in step S4 is as follows:

[0096]

[0097] Pij ≥0 (2);

[0098] Where, P ij is the power generation right shared by the sharing party and the receiving party, α gre is the transaction price of the green certificate market;

[0099] The set of power generation entities participating in sharing φ={N sha ,N rec}, where N sha N is the party sharing the power generation rights, rec The power generation right receiver, when there is no matching receiver or the number of sharing parties is 0, the single matching ends; and the matching type is determined according to the type of power generation right sharing party and power generation right receiver. When the power generation right sharing party is thermal power and the power generation right receiver is new energy, the new energy is wind power or photovoltaic power generation, so as to realize the priority consumption of new energy, increase the power generation of new energy, and thus increase the green certificate trading income. At the same time, the carbon quota saved by thermal power generation can be traded in the carbon market. The matching set is as follows:

[0100] N sha ={P car,w ,α car,w ,F car,w} (3);

[0101] N rec ={P gre,v ,α gre,v ,F gre,v} (4);

[0102] Where, P car,w The shareable amount of power generation rights declared by the sharing party, α car,w is the electricity price corresponding to the shareable amount of power generation rights, F car,w P is the income obtained by the power generation right sharing party after receiving it. gre,v is the power generation right declared by the power generation right recipient, α gre,v F is the green certificate price corresponding to the power generation right received by the power generation right receiver, gre,v The green certificate income obtained by the power generation right recipient after receiving the power generation right;

[0103] As a flexible energy storage method, hydropower stations can play a role in peak load shifting and valley filling in the power system, as well as optimizing resource allocation. During the matching process, hydropower stations declare the number of power generation rights they can receive based on their own energy storage conditions and needs. Therefore, when the power generation rights sharer is a thermal power plant and the power generation rights recipient is a hydropower plant, the matching set is as follows:

[0104] N rec ={P pum,v ,α pum,v ,F pum,v} (5);

[0105] Where, P pum,v The power generation right declared by the recipient of the power generation right (pumped storage), α pum,v is the electricity price corresponding to the power generation right received by the power generation right receiver, F pum,v The income from increased power generation obtained by the recipient of power generation rights after receiving the power generation rights;

[0106] The sharing matching rules of the power generation rights sharing mechanism include: (1) Maximizing demand matching: In each power generation rights sharing matching process, the sharing party and the receiving party prioritize reaching the maximum transaction amount to meet the receiving party's needs as soon as possible. Moreover, the needs of a receiving party can be met by multiple sharing parties. Conversely, the sharing party can also share the power generation rights with multiple receiving parties at the same time until all needs are met. (2) In order to reduce the carbon emissions of the multi-energy alliance and reduce carbon quota consumption, the thermal power generation rights will be transferred in full, and only the power generation corresponding to its minimum output will be retained to ensure the basic operation needs of the system. (3) Assuming that the daytime period is 7:00-19:00, since photovoltaic power generation is affected by sunlight conditions, the output has a significant diurnal periodicity. During the day, photovoltaic power generation has the greatest potential and the marginal power generation cost is extremely low. Therefore. The order of receiving power generation rights during the day is: photovoltaic, wind power, and hydropower. That is, photovoltaic power generation will first receive the power generation rights transferred by thermal power until photovoltaic power generation reaches the power generation limit; then wind power will receive the power generation rights transferred by thermal power until wind power reaches the power limit; finally, hydropower will receive the remaining power generation rights. If hydropower reaches its output limit, matching ends. Assuming the nighttime period is from 20:00 to 6:00 the next day, which is a dark period, photovoltaic power cannot generate electricity and will exit the sharing mechanism. Therefore, the order of receiving nighttime power generation rights is adjusted to: wind power, then hydropower. That is, wind power receives power generation rights first. After reaching the power generation limit, hydropower continues to receive the remaining power generation rights. If hydropower reaches its output limit, matching ends.

[0107] The objective function expression for maximizing the benefits of the multi-energy alliance is as follows:

[0108] max F all =F ele +F car +F gre (6);

[0109] Where, F all is the total benefit of the multi-energy alliance, F ele 、F car and F gre These are the profits of the multi-energy alliance from participating in the electricity spot market, carbon emission rights market and green certificate market respectively;

[0110] in,

[0111]

[0112] Fcar =α car R car (8);

[0113] F gre =α gre P gre (9);

[0114] Where, is the clearing settlement price of the multi-energy alliance in the electricity spot market during period t, is the cleared electricity quantity of the multi-energy alliance in the electricity spot market during period t, α car and R car are the transaction price of carbon emission rights market and the number of carbon quotas sold by multi-energy alliances, P gre =The number of green certificates traded within the multi-energy alliance. In this embodiment, wind and photovoltaic power generation entities can apply for one green certificate for every 1MWh of electricity generated and participate in the green certificate market. Specifically, wind and photovoltaic power generation operators apply for corresponding green certificates from the Renewable Energy Information Center based on their consumption volume. The Information Center issues green certificates with identification codes to qualified wind and photovoltaic companies based on the type of renewable energy generation, consumption time, power generation location, and project identification. Each green certificate represents 1MWh of renewable energy electricity and is priced at a fixed price of 42.2 yuan.

[0115] The power clearing model expression is as follows:

[0116]

[0117] In the formula, m represents the total number of power generation entities, a i,bid represents the bid of the i-th power generation entity, a L,bid is the unit cost of electricity purchase, P i t and They represent the clearing power and load value of power generation entity i during period t respectively;

[0118] The constraints of the multi-energy collaborative optimization model include power balance constraints, upper and lower power limits of each power generation entity, start and stop constraints of each power generation entity, and ramp constraints of each power generation entity. The power balance constraint expression is as follows:

[0119]

[0120] The upper and lower power constraint expressions of each power generation entity are as follows:

[0121]

[0122] Where, P i min and P i maxThey represent the lower and upper limits of the power clearing of power generation entity i during period t respectively;

[0123] The start and stop constraint expressions of each power generation entity are as follows:

[0124]

[0125] Where, P i represents the clearing power of power generation entity i, x i represents the start and stop status of the power generation entity i, and x i =0,1, when x i =1 indicates start, when x i =0 means stop;

[0126] The ramp constraint expressions of each power generation entity are as follows:

[0127]

[0128] Where, P i t-1 represents the clearing power of power generation entity i in period t-1, P i up and P i down They represent the upper and lower limits of the ramp rate of power generation entity i respectively;

[0129] Due to the constraints of the multi-energy alliance power generation rights sharing mechanism, the carbon emissions of thermal power plants in the multi-energy alliance shall not exceed their carbon quotas. car When it is positive, it means that its carbon quota is higher than its carbon emissions, and the remaining carbon quota can be sold to obtain additional economic benefits. car When it is negative, it means that the carbon emissions of the thermal power plant exceed its allocated quota and it needs to purchase carbon emission rights to meet the carbon emission standards. In order to encourage thermal power plants to actively reduce carbon emissions, there is a positive correlation between the trading volume of carbon emission rights and the trading price, that is, the more carbon emission rights purchased or sold, the higher the corresponding trading price. Therefore, the trading price of the carbon emission rights market is α car The expression is as follows:

[0130]

[0131] Where, α c 0 ar is the carbon trading benchmark price, d is the carbon trading range; δ is the carbon price growth rate. In this embodiment, the carbon trading benchmark price Take 68.73, the carbon trading interval d is 20, and the carbon price growth rate δ is 0.1.

[0132] Because electricity clearing models in the electricity spot market involve scheduling decisions for multiple generating units, they incorporate nonlinear relationships (such as the generator's cost function and the relationship between load demand and power generation). Nonlinear constraints and objective functions lead to nonconvexity in the model, making the electricity spot market optimization model a nonconvex nonlinear optimization problem. Furthermore, due to factors such as power demand fluctuations and the dynamic nonlinear feedback between market prices and power generation, the objective function is often nonlinear and may have multiple local optimal solutions, making it difficult to guarantee a global optimal solution directly. To address the complexity of nonconvex nonlinear models, the multi-energy collaborative optimization model is first solved using the KKT optimality condition reformulation and then using the large-M method.

[0133] The steps of the Big M method are as follows:

[0134] The first step is to reformulate the multi-energy collaborative optimization model using the KKT optimal conditions: According to the KKT conditions, the power clearing model is integrated into the constraint conditions, and the Lagrangian multiplier is introduced to combine the constraint conditions shown in formulas (10) to (14) with the objective function shown in formula (6) to form the Lagrangian function:

[0135]

[0136] Where λ b is the Lagrange multiplier for the power balance constraint; and are the Lagrange multipliers of the upper and lower power constraints of power generation entity i, and is the Lagrange multiplier for the lower and upper limits of the ramp rate of power generation entity i;

[0137] The second step is to linearize the reformulated multi-energy collaborative optimization model using the Big M method and solve the linear programming algorithm. The decision variables of the power clearing model are the quotation of participating in the power spot market. The output values ​​of wind power, photovoltaic power generation, hydropower and thermal power are The decision variable of the power generation rights sharing model is R car and P ij .

[0138] S5. Based on the multi-energy collaborative optimization model described in step S4, and under the condition that the total output of the multi-energy alliance described in step S2 remains unchanged, performing carbon quota sharing matching for each power generation entity;

[0139] Step S5 specifically includes the following steps:

[0140] S51. Obtaining carbon dioxide emissions from each power generation entity;

[0141] S52. Calculate the remaining quota based on the carbon emission standards;

[0142] S53. Implement quota exchange: Based on the power generation rights sharing mechanism, power generation entities with surplus quotas are allowed to sell them to power generation entities that lack quotas.

[0143] S6. Determine the output plan declared by the multi-energy alliance based on the carbon quota sharing matching result determined in step S5;

[0144] S7. The electricity spot market is cleared according to the output plan declared by the multi-energy alliance determined in step S6.

[0145] Simulation experiment

[0146] In this simulation experiment, Figure 2 and 3 Taking the IEEE 14-node system shown as an example, this simulation involves eight power plants participating in the electricity-carbon-green certificate market. The eight power plants are located at nodes 2, 4, 8, 11, and 14, respectively. Node 2 connects to Thermal Power Plants 1 and 2, node 4 connects to Wind Power Plants 1 and 2, node 8 connects to Photovoltaic Power Plants 1 and 2, node 11 connects to a hydropower station, and node 14 connects to Photovoltaic Power Plant 3. Thermal Power Plants 1 and 2, Wind Power Plant 1, Photovoltaic Power Plant 1, and the hydropower station together form a multi-energy alliance operating model to participate in the market. The carbon emission intensity is assumed to be 0.9 t / (MW·h), and the carbon emission calculation coefficients a, b, and c are 0.0034, -0.38, and 36, respectively.

[0147] Table 1 Parameters related to power generation

[0148] parameter No. 1 Thermal Power Plant No. 2 Thermal Power Plant Wind Power No. 1 Photovoltaic No. 1 hydropower Maximum power generation (MW) 280 240 200 200 150 Minimum power generation (MW) 40 60 0 0 20 Up and down ramp rate (MW) 60 60 40 40 50 <![CDATA[Initial carbon quota (tCO2)]]> 216 216 0 0 0

[0149] In combination with Table 1, two schemes are designed: Scheme 1 is to adopt the power generation right sharing mechanism within the alliance as described in the present invention, and Scheme 2 is not to consider the power generation right sharing mechanism within the alliance.

[0150] The hourly clearing price of the multi-energy alliance participating in the electricity spot market is as follows: Figure 4As shown, it can be seen that the clearing price change trends of the two schemes are basically the same. During the high-load period (10:00-13:00, 14:00-18:00), the clearing price remains at a high level; while in the low-load period (1:00-6:00, 19:00-24:00), due to the lack of photovoltaic output, most of the load is supported by wind power and hydropower within the multi-energy alliance, and the clearing price is relatively low. By introducing the sharing of power generation rights, Scheme 1 reduces the overall clearing price level of the multi-energy alliance and improves the market competitiveness of the multi-energy alliance (on the one hand, the sale of carbon quotas and green certificates brings additional income to the multi-energy alliance, making it more price competitive in the spot market to obtain more electricity trading shares. On the other hand, the low electricity price environment helps to reduce social energy costs and increase the proportion of clean energy in the terminal market).

[0151] Comparing the income analysis of Option 1 and Option 2, and comparing the income of the electricity-carbon-green certificate market of Option 1 and Option 2, we can clarify the contribution of participating in the diversified market to the income of the multi-energy alliance. The results are as follows: Figure 5 shown.

[0152] Overall benefit analysis: Figure 5 (a) It can be seen that, Scheme 1: The multi-energy alliance profit curve is higher than Scheme 2. In the 12:00 period, the maximum value of the multi-energy alliance's total profit is 150,100 yuan. From 11:00 to 24:00, a total of 14 time periods, the multi-energy alliance's total profit exceeds 90,000 yuan. Scheme 2: The maximum value of the multi-energy alliance's total profit is 138,900 yuan, which occurs in the 12:00 period. From 11:00 to 18:00 and 21:00, a total of 9 time periods, the multi-energy alliance's total profit exceeds 90,000 yuan. Compared with Scheme 2, the total profit of Scheme 1 increased by 11,200 yuan, and the number of time periods in which the multi-energy alliance's profit exceeded 90,000 yuan increased by 5. This proves that the power generation rights sharing mechanism described in the present invention can significantly improve the overall profit level of the multi-energy alliance.

[0153] Analysis of the benefits of alliance participation in multiple markets: Figure 5 (b) and (c) show the changing trends of various types of benefits and their contribution under different scenarios and mechanisms. Under Option 2, the alliance's total benefits are mainly concentrated in electricity revenue, while carbon revenue and green certificate revenue account for a relatively small proportion. However, after the introduction of the sharing mechanism, the overall benefits and benefit structure of the multi-energy alliance have changed significantly.

[0154] Looking at the revenue results, specifically the revenue sources, electricity revenue remains the primary source of income for the multi-energy alliance. Under Option 2, electricity revenue accounts for 80%-85% of total revenue, while under Option 1, this proportion drops to 65%-75%. For example, in the higher-revenue scenario, the electricity revenues from Thermal Power Plant 1, Thermal Power Plant 2, photovoltaic power, and wind power are 14,863.8 yuan, 33,600 yuan, 25,415 yuan, and 23,162.49 yuan, respectively, with the combined revenue accounting for 72.8% of total revenue. In contrast, the revenues from the carbon market and green certificate market have increased significantly with the introduction of the sharing mechanism.

[0155] Depend on Figure 5 As shown in Figures (d)-(f), while revenue has increased, the revenue contributions of various market types have also become more balanced and diversified. Contribution of the carbon market: Without the sharing mechanism, the carbon market's revenue contribution was only 10%-12%. After the sharing mechanism was introduced, its contribution increased to 15%-18%, even exceeding 20% ​​in some scenarios. In the highest-revenue scenario, the carbon revenue contributions of Power Plant 1 and Power Plant 2 were 16.64% and 12.9%, respectively. Contribution of the green certificate market: The green certificate market's contribution increased to 8%-10%, particularly in scenarios with high wind and solar power generation. In scenarios with 4,537.5 kWh of photovoltaic power generation and 6,837 kWh of wind power generation, green certificate revenue accounted for 9.8% of total revenue. Electricity revenue remains the core source of overall revenue, but its proportion has decreased from 80%-85% before the sharing mechanism to 65%-75%, indicating a significant increase in the driving effect of the carbon and green certificate markets on revenue.

[0156] Table 2 Tiered carbon price range

[0157] level <![CDATA[Carbon quota range / tCO2]]> Carbon price / yuan I (0,20] 68.73 II (20,40] (68.73，72.16] III (40,60] (72.16，75.61] IV (60,80] (75.61,79.03] V (80,216] 79.5

[0158] Based on the tiered carbon prices in Table 2, the remaining carbon quota trading volume and corresponding time of the multi-energy alliance's thermal power units in different periods are calculated, and the results are shown in Table 3.

[0159] Table 3 Remaining carbon quota trading volume and corresponding period

[0160]

[0161]

[0162] Table 3 shows that Unit 1's remaining carbon allowances are primarily concentrated in Level IV and Level V, while Unit 2's trading is more distributed across multiple peak periods with shorter time intervals. As carbon allowances accumulate and carbon prices gradually rise, generators are encouraged to optimize their power generation plans at higher carbon price levels, thereby reducing their consumption of high-priced allowances. Unit 1 has a larger trading load, while Unit 2 actively trades its remaining carbon allowances during low-load periods. Unit 1 reduces its carbon costs by fully utilizing low-priced allowances during high-load periods, demonstrating resource complementarity and benefit sharing within the multi-energy alliance.

[0163] like Figure 6 As shown in the figure, the output of each entity considering shared power generation rights is analyzed: Scenario 1: During peak hours, the output of Wind Farm 1 increases from 57.53MW to 99.5MW. At the same time, the output of Thermal Power Plants 1 and 2 decreases to 40MW each. During off-peak hours, the output of Wind Farm 1 increases from 52.73MW to 175.79MW, while the total output of thermal power plants decreases to 148MW.

[0164] Option 2: During peak hours, conventional thermal power generation maintains a high output level. Thermal Power Plants 1 and 2 each generate 66.58 MW, while the wind farm's output is lower, at 57.53 MW. During off-peak hours, thermal power generation output decreases but remains high. Thermal Power Plants 1 and 2 each generate 62.87 MW, while the wind farm's output is 52.73 MW.

[0165] In summary, compared to Option 2, Option 1 significantly improves the utilization rate of renewable energy. Wind farm output increased by 41.97 MW, while total thermal power output decreased by 53.16 MW. This demonstrates that the present invention can adjust and optimize the power supply structure, reduce carbon emissions from traditional thermal power generation, and increase the overall green energy content of the system, demonstrating the important role of power generation rights sharing in reducing carbon emissions.

[0166] like Figure 7 As shown in the figure, the analysis of carbon emission reduction and new energy consumption under the multi-energy alliance is as follows: Scenario 1: Carbon emissions decreased by more than 91.62 tons between 10:00 and 24:00, and carbon emissions dropped to 79.86 tons at 12:00, a decrease of 201.42 tons compared to Scenario 2. It can be seen that Scenario 1 significantly increased new energy consumption, with consumption during the 13:00 period increasing from 134.94MW to 360.79MW, an increase of 225.84MW.

[0167] Option 2: The multi-energy alliance under Option 2 has higher carbon emissions throughout the day, particularly between 10:00 AM and midnight, where emissions remain consistently high. Emissions during the 12:00 PM period reached 281.28 tons. In terms of new energy consumption, Option 2's capacity is relatively low, reaching 134.94 MW during the 1:00 PM period.

[0168] In summary, Scheme 1 demonstrated the advantages of reduced carbon emissions and increased renewable energy output throughout the day, with the most significant reductions and increased renewable energy consumption between 12:00 PM and 4:00 PM. This demonstrates that this invention can effectively control carbon emissions and improve renewable energy utilization efficiency through flexible carbon quota allocation and trading, further promoting the multi-energy alliance's transition toward a green, low-carbon future.

[0169] Analysis of the power generation rights sharing process within the alliance: The power generation rights sharing within the alliance is mainly centered on the power transfer sharing of power generation rights, and the results are shown in Table 4.

[0170] Table 4 Multi-scale power generation rights sharing matching table for each power generation entity

[0171]

[0172] Table 4 shows that between 1:00 AM and 11:00 AM and 7:00 PM and midnight, only Thermal Power Plant 1 participates in power generation rights sharing. During the rest of the time, both Thermal Power Plant 1 and Thermal Power Plant 2 participate. Furthermore, for Thermal Power Plant 1, wind farms and photovoltaic power plants are the primary users. During the 12:00 PM period, the maximum shared power between Thermal Power Plant 1 and the wind farm is 175.5 MW, while the minimum shared power between Thermal Power Plant 1 and the wind farm is 8.75 MW at 11:00 AM. For Thermal Power Plant 2, hydropower plants are the primary users, with the maximum shared power between the two reaching 92 MW. Therefore, Thermal Power Plants 1 and 2 form a stable match with wind farms, photovoltaic power plants, and hydropower plants.

[0173] At the same time, alliance operators share power generation rights based on the balance of electricity demand and supply. During peak electricity demand periods, thermal power plants transfer power generation rights to new energy power plants to meet overall electricity demand. During the period from 10:00 to 18:00, electricity demand is high. Based on the power generation rights sharing mechanism, thermal power plants transfer power generation space to promote cooperation and coordination among power plants. At the same time, the remaining carbon quotas of thermal power plants can participate in carbon market transactions, which increases alliance revenue and promotes the consumption of wind and solar new energy.

[0174] At 13:00, the maximum shared generation rights within the multi-energy alliance reached 361.02MW, and the maximum increase in the multi-energy alliance's revenue during this period was 33,215.42 yuan. At 7:00, the minimum shared generation rights within the multi-energy alliance reached 60.02MW, and the increase in the multi-energy alliance's revenue during this period was 2,820.53 yuan. This shows that in both extreme scenarios of maximum and minimum shared generation rights, the multi-energy alliance's revenue increase increased, demonstrating the effectiveness of the generation rights sharing mechanism described in this invention in optimizing resource allocation and increasing alliance revenue.

[0175] During the 11:00 AM period, the wind farm's maximum revenue increase was 2,413.36 yuan. However, during this period, the No. 1 thermal power plant only shared 8.75 MW of power generation rights with the wind farm. This was because the wind power output reached its upper limit during this period, preventing it from receiving more power rights to increase its revenue. Furthermore, the 11:00 AM period was the peak operating price for the Multi-Energy Alliance, allowing the wind farm to sell its electricity at a higher price, contributing to the overall increase in the Multi-Energy Alliance's revenue. During the 3:00 PM period, the maximum increase in photovoltaic revenue was 7,360.31 yuan. During this period, the thermal power plant's power generation rights were not shared with the photovoltaic power plant, but the photovoltaic power generation price was at its highest during this period, allowing the photovoltaic power plant to sell electricity at a higher price, which also contributed to the overall increase in the Multi-Energy Alliance's revenue.

[0176] In summary, in this simulation experiment, it was verified that the introduction of the power generation rights sharing mechanism can improve the level of new energy consumption and overall benefits within the multi-energy alliance, and the role of the multi-energy alliance operation system in promoting low-carbon development was verified; and the impact mechanism of the diversified market mechanism on the market clearing electricity quotation of the multi-energy alliance was analyzed, and it was verified that under the diversified market trading model, the electricity quotation of the multi-energy alliance in the market can be reduced and its market competitiveness can be improved; at the same time, the optimal matching relationship of multi-scale power generation rights between different power generation entities was analyzed and determined, and the effect of cooperative operation among multi-energy alliance members was improved; the impact of the power generation rights sharing matching mechanism on the multi-scale benefits of the multi-energy alliance was revealed, which provided a reference basis for the optimization decision-making of the low-carbon operation of the multi-energy alliance.

[0177] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A collaborative optimization operation method for a multi-energy alliance considering power generation rights sharing, characterized by: The following steps are involved: S1. Identify various types of power generation entities participating in power spot market transactions, and establish a multi-energy alliance for information sharing based on the identified various types of power generation entities; S2. Determine the total output of the multi-energy alliance based on the clearing results of the electricity spot market transactions described in step S1 to ensure that the total output of the multi-energy alliance is consistent with market demand; S3. Set research assumptions, including reasonable distribution of benefits, free electricity trading, participation of the multi-energy alliance as a whole in electricity spot market transactions, and a virtual sharing model for power generation rights sharing under the dual influence of market trading rules and physical constraints of each power generation entity; S4. Under the research assumptions set in step S3, a power generation rights sharing mechanism is introduced, and with the goal of maximizing the benefits of the multi-energy alliance, a multi-energy collaborative optimization model considering the electricity-carbon-green certificate market trading mechanism is constructed; The matching objective function of the power generation rights sharing mechanism described in step S4 is as follows: P ij ≥0 (2)； Where, P ij is the power generation right shared by the sharing party and the receiving party, α gre is the transaction price of the green certificate market; The set of power generation entities participating in sharing φ={N sha ,N rec }, where N sha N is the party sharing the power generation rights, rec The power generation right receiver is the power generation right receiver. The matching type is determined according to the types of the power generation right sharer and the power generation right receiver. When the power generation right sharer is thermal power and the power generation right receiver is renewable energy, the renewable energy is wind power or photovoltaic power generation, and the renewable energy is consumed first. The matching set is as follows: N sha ={P car,w ,a car,w ,F car,w } (3); N rec ={P gre,v ,a gre,v ,F gre,v } (4); Where, P car,w The shareable amount of power generation rights declared by the sharing party, α car,w is the electricity price corresponding to the shareable amount of power generation rights, F car,w P is the income obtained by the power generation right sharing party after receiving it. gre,v is the power generation right declared by the power generation right recipient, α gre,v F is the green certificate price corresponding to the power generation right received by the power generation right receiver, gre,v The green certificate income obtained by the power generation right recipient after receiving the power generation right; When the power generation rights sharing party is thermal power and the power generation rights receiving party is hydropower, the matching set is as follows: N rec ={P pum,v ,a pum,v ,F pum,v } (5); Where, P pum,v is the power generation right declared by the power generation right recipient, α pum,v is the electricity price corresponding to the power generation right received by the power generation right receiver, F pum,v The income from increased power generation obtained by the recipient of power generation rights after receiving the power generation rights; The sharing matching rules of the power generation rights sharing mechanism include: (1) maximizing demand matching. In each power generation rights sharing matching process, the sharing party and the receiving party prioritize reaching the maximum transaction amount; (2) all thermal power generation rights are transferred, and only the power generation corresponding to the minimum output is retained to ensure the basic operation needs of the system; (3) assuming that the daytime period is 7:00-19:00, the order of receiving daytime power generation rights is: photovoltaic power, wind power, hydropower; assuming that the nighttime period is 20:00-6:00 the next day, the order of receiving nighttime power generation rights is adjusted to: wind power, hydropower; The objective function expression for maximizing the benefits of the multi-energy alliance is as follows: maxF all =F ele +F car +F gre (6); Where, F all is the total benefit of the multi-energy alliance, F ele 、F car and F gre These are the profits of the multi-energy alliance from participating in the electricity spot market, carbon emission rights market and green certificate market respectively; in, F car =a car R car (8); F gre =α gre P gre (9); Where, is the clearing settlement price of the multi-energy alliance in the electricity spot market during period t, is the cleared electricity quantity of the multi-energy alliance in the electricity spot market during period t, α car and R car are the transaction price of carbon emission rights market and the number of carbon quotas sold by multi-energy alliances, P gre The number of green certificates traded within the Multi-Energy Alliance; The power clearing model expression is as follows: In the formula, m represents the total number of power generation entities, a i,bid represents the bid of the i-th power generation entity, a L,bid is the unit cost of electricity purchase, P i t and They represent the clearing power and load value of power generation entity i during period t respectively; The constraints of the multi-energy collaborative optimization model include power balance constraints, upper and lower power limits of each power generation entity, start and stop constraints of each power generation entity, and ramp constraints of each power generation entity. The power balance constraint expression is as follows: The upper and lower power constraint expressions of each power generation entity are as follows: Where, P i min and P i max They represent the lower and upper limits of the power clearing of power generation entity i during period t respectively; The start and stop constraint expressions of each power generation entity are as follows: Where, P i represents the clearing power of power generation entity i, x i represents the start and stop status of the power generation entity i, and x i =0,1, when x i =1 means start, when x i =0 means stop; The ramp constraint expressions of each power generation entity are as follows: Where, P i t-1 represents the clearing power of power generation entity i in period t-1, P i up and P i down They represent the upper and lower limits of the ramp rate of power generation entity i respectively; The solution method of the multi-energy collaborative optimization model is to first reformulate the KKT optimal conditions and then solve it using the big M method; S5. Based on the multi-energy collaborative optimization model described in step S4, and under the condition that the total output of the multi-energy alliance described in step S2 remains unchanged, performing carbon quota sharing matching for each power generation entity; S6. Determine the output plan declared by the multi-energy alliance based on the carbon quota sharing matching result determined in step S5; S7. The electricity spot market is cleared according to the output plan declared by the multi-energy alliance determined in step S6.

2. The method for collaborative optimization of multi-energy alliance operations considering power generation rights sharing according to claim 1, characterized in that: In step S1, multiple types of power generation entities include wind power, thermal power, hydropower and photovoltaic power generation, and information is shared among wind power, thermal power, hydropower and photovoltaic power generation; In step S2, the dispatch center monitors the clearing results.

3. The method for collaborative optimization of multi-energy alliance operations considering power generation rights sharing according to claim 2 is characterized by: The rationality of the benefit distribution in step S3 includes individual rationality, alliance rationality, and group rationality. Individual rationality means that the benefits distributed to the power generation entity within the multi-energy alliance are not less than the benefits it can obtain by operating independently; alliance rationality means that the benefits distributed to the power generation entity in the current multi-energy alliance are not less than the benefits distributed in other multi-energy alliances; group rationality means that the benefit distribution of all power generation entities in the multi-energy alliance meets the distribution optimization equilibrium; Freedom of electricity trading means that power generation entities within the multi-energy alliance can freely trade electricity to meet the balance of power supply and demand.

4. The method for collaborative optimization of multi-energy alliance operations considering power generation rights sharing according to claim 3 is characterized by: The transaction price of carbon emission rights market α car The expression is as follows: Where, is the carbon trading benchmark price, d is the carbon trading range; δ is the carbon price growth rate.

5. The method for collaborative optimization of multi-energy alliance operations considering power generation rights sharing according to claim 4 is characterized by: The steps of the Big M method are as follows: The first step is to reformulate the multi-energy collaborative optimization model using the KKT optimal conditions: According to the KKT conditions, the power clearing model is integrated into the constraint conditions, and the Lagrangian multiplier is introduced to combine the constraint conditions shown in formulas (10) to (14) with the objective function shown in formula (6) to form the Lagrangian function: Where λ b is the Lagrange multiplier for the power balance constraint; and are the Lagrange multipliers of the upper and lower power constraints of power generation entity i, and is the Lagrange multiplier for the lower and upper limits of the ramp rate of power generation entity i; The second step is to linearize the reformulated multi-energy collaborative optimization model using the Big M method and solve the linear programming algorithm. The decision variables of the power clearing model are the quotation of participating in the power spot market. The output values ​​of wind power, photovoltaic power generation, hydropower and thermal power are The decision variable of the power generation rights sharing model is R car and P ij .

6. The method for collaborative optimization of multi-energy alliance operations considering power generation rights sharing according to claim 5, characterized in that: Step S5 specifically includes the following steps: S51. Obtaining carbon dioxide emissions from each power generation entity; S52. Calculate the remaining quota based on the carbon emission standards; S53. Implement quota exchange: Based on the power generation rights sharing mechanism, power generation entities with surplus quotas are allowed to sell them to power generation entities that lack quotas.

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