Multi-energy alliance collaborative optimization operation method considering power generation right sharing

Abstract

The invention discloses a multi-energy alliance collaborative optimization operation method considering power generation right sharing, and belongs to the field of multi-energy collaborative power generation operation, and the method comprises the following steps: S1, building an information sharing multi-energy alliance; s2, determining the total output of the multi-energy alliance according to the clearing result; s3, setting a research hypothesis condition; s4, constructing a multi-energy collaborative optimization model considering an electricity-carbon-green certificate market transaction mechanism; s5, on the basis of the multi-energy collaborative optimization model, under the condition that the total output of the multi-energy alliance is kept unchanged, carbon quota sharing matching is carried out on each power generation main body; s6, determining a declaration output plan of the multi-energy alliance; and S7, clearing according to the declaration output plan of the multi-energy alliance. By adopting the multi-energy alliance collaborative optimization operation method considering power generation right sharing, power spot market clearing is participated in an alliance form, alliance internal resource configuration is optimized through power generation right sharing and green certificate transaction, and economic benefits and new energy consumption capability are improved.

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 developed into important means to improve energy system efficiency and promote renewable energy consumption. However, in actual operation, it still faces 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 complementary systems and carbon market mechanisms is of great significance for achieving low-carbon and efficient operation of energy systems.

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

[0004] First, we should optimize the energy supply and demand balance structure around the diversified energy demand of end users and combine the complementary characteristics of traditional energy and new energy to achieve efficient resource allocation. For example, we can achieve multi-energy coordinated supply and energy cascade utilization by means of multi-level combined supply or energy integration systems such as natural gas, heat, electricity, and cold. For example, Li Peng, Wang Zixuan, Wang Jiahao, etc. proposed a multi-time-space scale energy supply and demand balance method based on three levels: day-ahead, intraday, and real-time in "A multi-time-space scale optimal operation strategy for a distributed integrated energy system". Lu Qing, Guo Qisheng, Zeng Wei in "Optimization scheduling of integrated energy service system in community: A bi-layer optimization model considering multi-energy demand response and usersatisfaction", and Wang Xingang, Zhao Fang, Zhu Wenjun in "Analysis of regional energy consumption characteristics based on comprehensive energy metering data" disclosed the analysis of multi-energy demand response of community users, and improved the economy of comprehensive energy consumption and user satisfaction by optimizing user energy consumption behavior. Liu Hang, Zhao Haipeng, Wenming, etc., in "Station-grid-load collaborative planning method for integrated energy system considering flexible load distribution", and Zhao Haipeng, Miao Shihong, Li Chao, etc., in "Study on optimal operation strategy of integrated energy system in park considering coupling response characteristics of cooling, heating and electricity demand", constructed a collaborative optimization planning model under integrated energy or source-grid-load mode, and studied the case studies to show that the proposed method can effectively reduce system investment or system transmission energy consumption. At the same time, the existing research on multi-energy complementary economic optimization dispatch mainly focuses on model construction, optimal combination of power generation of each subject, and benefit compensation mechanism of each subject.

[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 motivate long-term collaboration and benefit sharing mechanisms among various entities is not in-depth enough.

[0006] The second is about the design of multi-level market mechanisms and coordinated coupling. For example, Li Bingyang, Li Xinli, Yang Guotian, etc. further discussed the joint optimization of multi-energy power generation systems in the electricity market and ancillary service market in "Optimal Dispatch of Power System for Joint Supply of Wind, Photovoltaic, Hydropower and Hydropower Storage to Thermal Power Plants" and Li X, Tan Z, Shen J, etc. in "Research on the operation strategy of joint wind-photovoltaic-hydropower-pumped storage participation in electricity market based on Nash negotiation". Wang Rongmao, Liu Miao, Zhang Ye, etc. constructed a multi-energy system dispatch model under a single carbon price in "Low-carbon Dispatch Technology of Wind, Photovoltaic, Hydropower and Hydropower Storage Systems Based on Carbon Trading and Carbon Capture Balanced Cost", and discussed the guiding role of carbon trading mechanism 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 be reflected in the real-time supply and demand relationship of market trading entities.

[0007] Wang Kun, Xu Chengwei, and Wen Fushuan proposed a strategy for new energy power generation companies to participate in the electricity spot market in the green certificate trading system in the "Transition Mechanism for Renewable Energy to Participate in the Spot Market under Green Certificate Trading", and verified the feasibility of the scheme to increase the income of new energy power generation companies through simulation experiments, providing a new idea for them to reduce their dependence on government subsidies. Zhang Hong, Meng Qingyao, Ma Hongjun, etc. further studied the economic low-carbon dispatch strategy of the power system considering the demand for green certificates in the "Economic Low-carbon Dispatch Strategy of the Inter-regional Interconnected System for Increasing the Demand for Green Certificates", and proposed a green certificate price setting mechanism based on supply and demand. Liu Kezhen, Dai Yinghao, Zhao Qingli, etc. established a cross-provincial trading model for new energy operators that comprehensively considers carbon trading and green certificate trading in the "New Energy Cross-provincial Trading Model Considering Carbon-Green Certificate Trading Mechanism". By introducing the above two mechanisms, the wind power and photovoltaic energy abandonment rates were significantly reduced.

[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 benefit 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 multi-level market mechanisms 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 taking into account the sharing of power generation rights to solve the above-mentioned technical problems.

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

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

[0012] S2. According to the clearing result of the electricity spot market transaction described in step S1, determine the total output of the multi-energy alliance 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, multi-energy alliances participating in electricity spot market transactions as a whole, and the power generation rights sharing mechanism adopting a virtual sharing mode 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, the power generation rights sharing mechanism is introduced, and the multi-energy collaborative optimization model considering the electricity-carbon-green certificate market trading mechanism is constructed with the goal of maximizing the benefits of the multi-energy alliance;

[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, carbon quota sharing matching is performed for each power generation entity;

[0016] S6. Determine the output plan declared by the multi-energy alliance according to 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 reasonableness 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 subject in the multi-energy alliance are not less than the benefits it can obtain from independent operation; alliance rationality means that the benefits distributed to the power generation subject 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 subjects 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 right 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 right, 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 new energy, the new energy is wind power or photovoltaic power generation, and the new energy is preferentially consumed. 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 is 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 is the green certificate price corresponding to the power generation right received by the power generation right receiver, F gre,v The green certificate income obtained by the recipient of power generation rights after receiving the power generation rights;

[0030] When the party sharing the power generation right is thermal power and the party receiving the power generation right 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 revenue from the increased electricity generated by the recipient of the 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 shall prioritize reaching the maximum transaction amount; (2) all thermal power generation rights shall be transferred, and only the power generation corresponding to the minimum output shall be retained to ensure the basic operation requirements of the system; (3) assuming that the daytime period is 7:00-19:00, the order of receiving daytime power generation rights is: photovoltaic, 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 of maximizing the benefits of the multi-energy alliance is as follows:

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

[0036] In the formula, F all is the total revenue of the multi-energy alliance, F ele 、F car and F gre They are the income of the multi-energy alliance 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] In the formula, is the clearing settlement price of the multi-energy alliance in the electricity spot market during period t, is the clearing 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 amount of carbon quota 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 clearing power of power generation entity i during period t respectively;

[0050] The start and 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 indicates start, when x i =0 means stop;

[0053] The climbing 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 it using the KKT optimal conditions and then solve it using the big M method.

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

[0058]

[0059] In the formula, 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 condition: According to the KKT condition, the power clearing model is integrated into the constraint condition, and the Lagrangian multiplier is introduced to combine the constraint conditions shown in formula (10)-formula (14) with the objective function shown in formula (6) to form a Lagrangian function:

[0062]

[0063] In the formula, 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 use the big M method to linearize the reformulated multi-energy collaborative optimization model 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 respectively The decision variable of the power generation right sharing model is R car and P ij .

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

[0066] S51, obtaining carbon dioxide emissions of each power generation entity;

[0067] S52. Calculate the remaining quota according to the carbon emission standard;

[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 capital circulation efficiency within the alliance is improved, and win-win cooperation among all parties is promoted;

[0071] 2. Promote the development of clean energy: Special emphasis is placed on the principle of giving priority to 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 the carbon emission rights market, it directly supports the carbon emission reduction target and contributes to addressing climate change;

[0073] 4. Enhance system flexibility: Allowing the order of receiving power generation rights to be flexibly adjusted (photovoltaic-wind power-hydropower-thermal power), increasing the adaptability and response speed of the system, and being able to respond quickly in the face of emergencies.

[0074] In summary, this invention explores the sharing mechanism of power generation rights within the multi-energy alliance by constructing a power generation rights sharing platform to promote carbon emission reduction, optimize economic benefits, and improve the absorption capacity of new energy. At the same time, the synergy between various heterogeneous energy sources such as wind power, photovoltaics, hydropower and thermal power and their impact on the overall optimization goals of the system are analyzed, and the supporting role of the power generation rights sharing mechanism in the optimization process is verified; it is intended to lay the foundation for obtaining the optimal power generation combination, high-yield operation and efficiency improvement of the multi-energy alliance, in order 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 A flowchart of a multi-energy alliance collaborative optimization operation method considering power generation right sharing according to the present invention;

[0077] Figure 2 It is the load curve diagram 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 multi-market benefit comparison result diagram described in the simulation experiment, wherein (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 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, wherein (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 purpose, technical scheme and advantages disclosed in the embodiments of the present invention clearer, the embodiments of the present invention are further described in detail in combination 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 used 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 reference numerals 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 explicitly listed, but may include other steps or units that are not explicitly listed or inherent to these processes, methods, products or devices.

[0085] The embodiments of the present invention are described in detail below in conjunction with 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. Determine various types of power generation entities participating in power spot market transactions, and establish a multi-energy alliance for information sharing based on the various types of power generation entities determined;

[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. According to the clearing result of the electricity spot market transaction described in step S1, determine the total output of the multi-energy alliance 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. Set research assumptions, including reasonable distribution of benefits, free electricity trading, multi-energy alliances participating in electricity spot market transactions as a whole, and the power generation rights sharing mechanism adopting a virtual sharing model under the dual influence of market trading rules and physical constraints of each power generation entity. The virtual sharing model can effectively realize information sharing, shared matching of power generation rights and optimized decision-making functions, and support the flexible and collaborative operation of multi-energy alliances in diversified market transactions;

[0092] Assuming that there are multiple types of cooperation among the four energy sources of wind power, photovoltaic power generation, hydropower and thermal power, and each subject expects to obtain benefits under different combinations, in order to achieve the stability of alliance operation, the reasonable distribution of benefits described in step S3 includes individual rationality, alliance rationality and group rationality, among which individual rationality means that the benefits obtained by the power generation subject in the multi-energy alliance are not less than the benefits it can obtain from independent operation; alliance rationality means that the benefits obtained by the power generation subject in the current multi-energy alliance are not less than the benefits obtained 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 subjects 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, the power generation rights sharing mechanism is introduced, and the multi-energy collaborative optimization model considering the electricity-carbon-green certificate market trading mechanism is constructed with the goal of maximizing the benefits of the multi-energy alliance;

[0095] Since only thermal power plants have carbon quotas in 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, so 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 right, rec The power generation right receiver, when the unmatched receiver or the sharing party is 0, the single matching ends; and the matching type is determined according to the type of the power generation right sharing party and the 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 then increase the green certificate trading income. At the same time, the carbon quota saved by thermal power 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 is 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 is the green certificate price corresponding to the power generation right received by the power generation right receiver, F gre,v The green certificate income obtained by the recipient of power generation rights after receiving the power generation rights;

[0103] As a flexible energy storage method, hydropower stations can play a role in peak load shaving and valley filling and resource optimization in the power system. In 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 sharing party is thermal power and the power generation rights receiving party is hydropower, 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 revenue from the increased electricity generated by the recipient of the 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 give priority to reaching the maximum transaction amount to meet the receiving party's needs as soon as possible. And 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 the consumption of carbon quotas, the thermal power generation rights will be fully transferred, 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 sunshine conditions, the output has obvious 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 give priority to receiving the power generation rights transferred by thermal power until photovoltaic power 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 the hydropower reaches the output limit, the matching ends. Assuming that the night time period is 20:00-6:00 the next day, this period is a period without sunlight. Since photovoltaic power cannot generate electricity, it will exit the sharing mechanism. Therefore, the order of receiving night power generation rights is adjusted to: wind power, hydropower. That is, wind power has priority in receiving power generation rights. After reaching the power generation limit, hydropower continues to receive the remaining power generation rights. If hydropower reaches the output limit, the matching ends;

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

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

[0109] In the formula, F all is the total revenue of the multi-energy alliance, F ele 、F car and F gre They are the income of the multi-energy alliance 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] In the formula, is the clearing settlement price of the multi-energy alliance in the electricity spot market during period t, is the clearing 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 amount of carbon quota sold by multi-energy alliances, P gre It is the number of green certificates traded within the multi-energy alliance; in this embodiment, for every 1MWh of electricity generated by wind power and photovoltaic power generation entities, one green certificate can be applied for and participate in the green certificate market transaction. Specifically, wind power and photovoltaic power generation operators apply for corresponding green certificates from the Renewable Energy Information Center based on their consumption. The Information Center issues green certificates with identification codes to qualified wind power and photovoltaic companies based on the type of new energy generation, consumption time, power generation location and project identification. Each green certificate represents 1MWh of new energy electricity, and each certificate has 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 clearing power 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 climbing 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] In the formula, α c 0 ar is the carbon trading benchmark price, d is the carbon trading interval; 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] Since the electricity clearing model of the electricity spot market involves the scheduling decisions of multiple power generation units, which contains nonlinear relationships (the cost function of the generator, the relationship between load demand and power generation), the nonlinear constraints and objective functions lead to the non-convex characteristics of the model, so the optimization model of the electricity spot market belongs to a non-convex nonlinear optimization problem. At the same time, under the influence of factors such as power demand fluctuations and dynamic nonlinear feedback between market electricity prices and power generation, the objective function is usually nonlinear and may have multiple local optimal solutions, so it is difficult to guarantee the global optimal solution when solving directly. In order to solve the complexity in the non-convex nonlinear model, the solution method of the multi-energy collaborative optimization model is to first use the KKT optimal condition to restate it, and then use the big M method to solve it.

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

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

[0135]

[0136] In the formula, 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 use the big M method to linearize the reformulated multi-energy collaborative optimization model 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 respectively The decision variable of the power generation right 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, carbon quota sharing matching is performed for each power generation entity;

[0139] Step S5 specifically includes the following steps:

[0140] S51, obtaining carbon dioxide emissions of each power generation entity;

[0141] S52. Calculate the remaining quota according to the carbon emission standard;

[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 according to 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, 8 power plants are simulated to participate in the electricity-carbon-green certificate market transaction, and the 8 power plants are located at nodes 2, 4, 8, 11, and 14 respectively. Among them, node 2 is connected to thermal power plant No. 1 and thermal power plant No. 2, node 4 is connected to wind power plant No. 1 and wind power plant No. 2, node 8 is connected to photovoltaic power station No. 1 and photovoltaic power station No. 2, node 11 is connected to hydropower station, and node 14 is connected to photovoltaic power station No. 3. And thermal power plants No. 1 and 2, wind power plant No. 1, photovoltaic power station No. 1 and hydropower station together constitute a multi-energy alliance operation mode to participate in market operation. Set the carbon emission intensity to 0.9t / (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 described in the present invention, and Scheme 2 does not 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 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 in 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 benefits to the multi-energy alliance, so that it can be 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] Compare the revenue analysis of Scheme 1 and Scheme 2, and compare the revenue of the electricity-carbon-green certificate market of Scheme 1 and Scheme 2, and clarify the contribution of participating in the diversified market to the revenue of the multi-energy alliance. The results are as follows Figure 5 shown.

[0152] Overall revenue 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 revenue is 150,100 yuan. From 11:00 to 24:00, a total of 14 time periods, the total revenue of the multi-energy alliance exceeded 90,000 yuan. Scheme 2: The maximum value of the multi-energy alliance's total revenue 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 total revenue of the multi-energy alliance exceeded 90,000 yuan. Compared with Scheme 2, the total revenue of Scheme 1 increased by 11,200 yuan, and the number of time periods in which the multi-energy alliance's revenue 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 revenue level of the multi-energy alliance.

[0153] Analysis of the benefits of alliance participation in multiple markets: Figure 5 (b) and (c) show that the various types of benefits and their contribution vary with different scenarios and mechanisms. In the case of Scheme 2, the total benefits of the alliance are mainly concentrated in the electricity benefits, while the proportion of carbon benefits and green certificate benefits is relatively low. After the introduction of the sharing mechanism, the overall benefits and benefit structure of the multi-energy alliance have changed significantly.

[0154] From the perspective of revenue results, when it comes to revenue sources, electricity revenue is still the main source of income for the Multi-Energy Alliance. When Option 2 is adopted, electricity revenue accounts for 80%-85% of the total revenue, while after Option 1 is adopted, the proportion drops to 65%-75%. For example, in the scenario with higher revenue, the electricity revenue of Thermal Power Plant No. 1, Thermal Power Plant No. 2, photovoltaic power and wind power are 14,863.8 yuan, 33,600 yuan, 25,415 yuan and 23,162.49 yuan respectively, and the total electricity revenue accounts for 72.8% of the total revenue. In contrast, the revenue of the carbon market and the green certificate market has increased significantly with the introduction of the sharing mechanism.

[0155] Depend on Figure 5 (d)-(f) show that while the revenue has increased, the revenue contribution of various markets has also become more balanced and diversified. Contribution of the carbon market: When the sharing mechanism is not considered, the contribution of the carbon market revenue is only 10%-12%, but after the introduction of the sharing mechanism, its contribution has increased to 15%-18%, and even reached more than 20% in some scenarios. In the scenario with the highest revenue, the carbon revenue contribution of Thermal Power Plant No. 1 and Thermal Power Plant No. 2 is 16.64% and 12.9% respectively. Contribution of the green certificate market: The contribution of the green certificate market has increased to 8%-10%, especially in the scenario with large wind and solar power generation. In the scenario with photovoltaic power generation of 4,537.5kWh and wind power generation of 6,837kWh, the green certificate revenue accounts for 9.8% of the total revenue. Electricity revenue is still the core source of overall revenue, but its proportion has dropped from 80%-85% when the sharing mechanism is not considered to 65%-75%, indicating that the driving effect of the carbon market and the green certificate market on revenue has been significantly enhanced.

[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] According to the tiered carbon prices in Table 2, the remaining carbon quota trading volume and corresponding time of the thermal power units of the Multi-Energy Alliance 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] As shown in Table 3, the remaining carbon quota of Unit 1 is mainly concentrated in Level IV and Level V, while the trading of Unit 2 is more distributed in multiple peak hours with shorter time intervals. With the cumulative use of carbon quotas, the carbon price gradually rises, prompting the generators to optimize the power generation plan as much as possible at higher tier carbon prices, thereby reducing the consumption of high-priced carbon quotas. Unit 1 has undertaken a larger trading task volume, Unit 2 actively trades the remaining carbon quota during low-load periods, and Unit 1 reduces carbon costs by making full use of low-priced carbon quotas during high-load periods, reflecting the resource complementarity and benefit sharing within the multi-energy alliance.

[0163] like Figure 6 As shown in the figure, the output analysis of each entity considering the sharing of power generation rights is as follows: Scheme 1: During peak hours, the output of wind farm No. 1 increases from 57.53MW to 99.5MW. At the same time, the output of thermal power No. 1 and No. 2 is reduced to 40MW respectively; during off-peak hours, the output of wind farm No. 1 increases from 52.73MW to 175.79MW, while the total output of thermal power decreases to 148MW.

[0164] Option 2: During peak hours, the output of traditional thermal power plants remains at a high level. The output of thermal power plants No. 1 and No. 2 is 66.58MW, while the output of the wind farm is lower, only 57.53MW. During low load periods, the output of thermal power plants decreases, but still remains at a high level. The output of thermal power plants No. 1 and No. 2 is 62.87MW, and the output of the wind farm is 52.73MW.

[0165] In summary, compared with Scheme 2, Scheme 1 significantly improves the utilization rate of renewable energy. The output of the wind farm increased by 41.97MW, while the total output of thermal power decreased by 53.16MW. This proves that the present invention can adjust and optimize the power structure, reduce the carbon emissions of traditional thermal power, and increase the proportion of green energy in the system as a whole, showing the important role of the power generation right sharing mechanism in reducing carbon emissions.

[0166] like Figure 7 As shown in the figure, the analysis of carbon emission reduction and new energy consumption of the multi-energy alliance is as follows: Scheme 1: From 10:00 to 24:00, the reduction in carbon emissions exceeded 91.62 tons, and the carbon emissions at 12:00 dropped to 79.86 tons, which was 201.42 tons less than that of Scheme 2. It can be seen that in terms of new energy consumption, the consumption of Scheme 1 has increased significantly, and the consumption at 13:00 has increased from 134.94MW to 360.79MW, an increase of 225.84MW.

[0167] Option 2: The carbon emissions of the multi-energy alliance in Option 2 are relatively high during all periods of the day, especially from 10:00 to 24:00, when carbon emissions remain high. Among them, the carbon emissions during the 12:00 period are 281.28 tons. In terms of new energy consumption, the consumption of Option 2 is relatively low, with a consumption of 134.94MW during the 13:00 period.

[0168] In summary, in all periods of the day, Scheme 1 shows the advantages of reducing carbon emissions and increasing the output of new energy, especially in the period from 12:00 to 16:00, the effects of reducing carbon emissions and absorbing new energy are most significant. This proves that the present invention can effectively control carbon emissions and improve the utilization efficiency of new energy through flexible carbon quota allocation and trading, and further promote the transformation of the multi-energy alliance towards green and low-carbon.

[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] As shown in Table 4, only Thermal Power Plant No. 1 participates in the sharing of power generation rights from 1:00 to 11:00 and from 19:00 to 24:00, while Thermal Power Plant No. 1 and Thermal Power Plant No. 2 both participate in the sharing of power generation rights in the rest of the time periods. And for Thermal Power Plant No. 1, wind power plants and photovoltaic power plants are the main sharing objects. In the 12:00 period, the maximum sharing value between Thermal Power Plant No. 1 and wind power plants is 175.5MW, and the minimum sharing value is 8.75MW shared by Thermal Power Plant No. 1 and wind power plants in the 11:00 period. For Thermal Power Plant No. 2, hydropower stations are the main sharing objects, and the maximum power generation rights sharing between the two is 92MW. Therefore, Thermal Power Plants 1 and 2 form a stable match with wind power plants, photovoltaic power plants, and hydropower stations.

[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 higher. Based on the power generation rights sharing mechanism, thermal power plants give up 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 amount of power generation rights shared within the multi-energy alliance was 361.02MW, and the maximum value of the multi-energy alliance's revenue increase during this period was 33,215.42 yuan; at 7:00, the minimum amount of power generation rights shared within the multi-energy alliance was 60.02MW, and the multi-energy alliance's revenue increase during this period was 2,820.53 yuan. It can be seen that in the two extreme scenarios of the maximum or minimum power generation rights sharing, the multi-energy alliance's revenue increment has increased, which proves the effectiveness of the power generation rights sharing mechanism described in the present invention in optimizing resource allocation and improving alliance revenue.

[0175] During the 11:00 period, the maximum increase in wind farm revenue was 2,413.36 yuan, but during this period, the power generation rights shared by Thermal Power Plant No. 1 with the wind farm were only 8.75MW. This was because the wind power output reached the upper limit during this period and could not receive more power generation rights to increase revenue. In addition, the 11:00 period was the highest point in the operating electricity price of the Multi-Energy Alliance. The wind farm was able to sell its electricity at a higher price, contributing to the overall increase in the revenue of the Multi-Energy Alliance. During the 15:00 period, the maximum increase in photovoltaic revenue was 7,360.31 yuan. During this period, the power generation rights of the thermal power plant were not shared with photovoltaic power, but the photovoltaic power generation price was the highest during this period. The photovoltaic power station sold electricity at a higher price, which also promoted the increase in the overall revenue of the Multi-Energy Alliance.

[0176] In summary, in this simulation experiment, it is 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. The impact mechanism of the multi-market mechanism on the market clearing electricity quotation of the multi-energy alliance is analyzed, and it is verified that under the multi-market trading model, the electricity quotation of the multi-energy alliance participating 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 is analyzed and determined, which improves the effect of cooperative operation among members of the multi-energy alliance; the influence of the power generation rights sharing matching mechanism on the multi-scale benefits of the multi-energy alliance is revealed, which provides a reference 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 solution of the present invention rather than to limit it. 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 solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A multi-energy alliance collaborative optimization operation method considering power generation right sharing, characterized by: The following steps are involved: S1. Determine various types of power generation entities participating in power spot market transactions, and establish a multi-energy alliance for information sharing based on the various types of power generation entities determined; S2. According to the clearing result of the electricity spot market transaction described in step S1, determine the total output of the multi-energy alliance 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, multi-energy alliances participating in electricity spot market transactions as a whole, and the power generation rights sharing mechanism adopting a virtual sharing mode 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, the power generation rights sharing mechanism is introduced, and the multi-energy collaborative optimization model considering the electricity-carbon-green certificate market trading mechanism is constructed with the goal of maximizing the benefits of the multi-energy alliance; 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, carbon quota sharing matching is performed for each power generation entity; S6. Determine the output plan declared by the multi-energy alliance according to 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. According to claim 1, a multi-energy alliance collaborative optimization operation method considering power generation right sharing is characterized by: 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. A multi-energy alliance collaborative optimization operation method considering power generation right sharing according to claim 2, characterized in that: The rationality of the benefit distribution in step S3 includes individual rationality, alliance rationality and group rationality, where individual rationality means that the benefits distributed to the power generation subject in the multi-energy alliance are not less than the benefits it can obtain from independent operation; alliance rationality means that the benefits distributed to the power generation subject 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 subjects 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. A multi-energy alliance collaborative optimization operation method considering power generation right sharing according to claim 3, characterized in that: The matching objective function of the power generation right 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 right, 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 new energy, the new energy is wind power or photovoltaic power generation, and the new energy is preferentially consumed. 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 is 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 is the green certificate price corresponding to the power generation right received by the power generation right receiver, F gre,v The green certificate income obtained by the recipient of power generation rights after receiving the power generation rights; When the party sharing the power generation right is thermal power and the party receiving the power generation right 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 revenue from the increased electricity generated by the recipient of the 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 shall prioritize reaching the maximum transaction amount; (2) all thermal power generation rights shall be transferred, and only the power generation corresponding to the minimum output shall be retained to ensure the basic operation requirements of the system; (3) assuming that the daytime period is 7:00-19:00, the order of receiving daytime power generation rights is: photovoltaic, wind power, and 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 of maximizing the benefits of the multi-energy alliance is as follows: maxF all =F ele +F car +F gre (6); In the formula, F all is the total revenue of the multi-energy alliance, F ele 、F car and F gre They are the income of the multi-energy alliance 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); In the formula, is the clearing settlement price of the multi-energy alliance in the electricity spot market during period t, is the clearing 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 amount of carbon quota 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 clearing power 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 indicates start, when x i =0 means stop; The climbing 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 it using the KKT optimal conditions and then solve it using the big M method.

5. A multi-energy alliance collaborative optimization operation method considering power generation right sharing according to claim 4, characterized in that: The transaction price of carbon emission rights market α car The expression is as follows: In the formula, is the carbon trading benchmark price, d is the carbon trading range; δ is the carbon price growth rate.

6. A multi-energy alliance collaborative optimization operation method considering power generation right sharing according to claim 5, characterized in that: The steps of Big M method are as follows: The first step is to reformulate the multi-energy collaborative optimization model using the KKT optimal condition: According to the KKT condition, the power clearing model is integrated into the constraint condition, and the Lagrangian multiplier is introduced to combine the constraint conditions shown in formula (10)-formula (14) with the objective function shown in formula (6) to form a Lagrangian function: In the formula, 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 use the big M method to linearize the reformulated multi-energy collaborative optimization model 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 respectively The decision variable of the power generation right sharing model is R car and P ij .

7. A multi-energy alliance collaborative optimization operation method considering power generation right sharing according to claim 6, characterized in that: Step S5 specifically includes the following steps: S51, obtaining carbon dioxide emissions of each power generation entity; S52. Calculate the remaining quota according to the carbon emission standard; 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.

Images