Multi-energy alliance income-risk collaborative distribution system based on improved Shapley value method

Abstract

The invention discloses a multi-energy alliance income-risk collaborative distribution system based on an improved Shapley value method, and belongs to the field of multi-market collaborative optimization, and the system comprises a multi-dimensional contribution degree calculation module which is used for calculating a low-carbon contribution degree, a comprehensive income contribution degree and a cost contribution degree; the risk assessment and correction module is used for dynamically correcting the Shapley value to obtain a corrected Shapley value, namely an alliance income distribution result; the dynamic adjustment module is used for executing Shapley value redistribution considering the comprehensive contribution degree of different power generation main bodies to obtain a redistribution result, and adjusting the redistribution result according to the difference value between the comprehensive contribution degree and the average contribution degree of all the power generation main bodies; and a data interface module. By adopting the multi-energy alliance income-risk collaborative distribution system based on the improved Shapley value method, the income fairness and the operation stability of the multi-energy alliance are remarkably improved through multi-dimensional contribution quantification, dynamic risk correction and a differential incentive mechanism, and the system has important theoretical significance and engineering application value.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-market collaborative optimization, and particularly to a multi-energy alliance revenue-risk collaborative distribution system based on an improved Shapley value method. Background Art

[0002] In the complex environment where the electricity-carbon-green certificate multi-markets are mutually coupled, traditional revenue distribution methods, such as the basic Shapley value method, have significant limitations. This method only focuses on the single dimension of economic benefits and fails to comprehensively consider the multi-dimensional characteristics in the operation of the multi-energy alliance. Specifically, it is manifested as follows:

[0003] 1. Lack of low-carbon contribution dimension: The existing methods do not establish a direct correlation between carbon emission reduction and revenue distribution. For example, the carbon quota surplus generated by thermal power plants through reducing fossil energy consumption and the green certificate revenue generated by new energy power generation are not quantitatively reflected in the distribution mechanism, resulting in a revenue distribution deviation of 28%-35% for new energy entities.

[0004] 2. Insufficient quantification of cost risks: The traditional model assumes that the marginal costs of each entity are fixed, ignoring the differentiated cost characteristics in actual operation. Taking pumped storage as an example, the start-stop costs (about 0.08 yuan / kWh) and equipment losses (the annual maintenance cost accounts for 6%-8% of the total investment) generated when it provides peak shaving services are not reasonably compensated in the basic Shapley value method. This asymmetry of cost risks may lead to a decrease of more than 30% in the participation enthusiasm of pumped storage entities.

[0005] 3. Lag in response to market fluctuations: When there are sharp fluctuations in carbon prices or sudden changes in green certificate demand, the traditional distribution method cannot be adjusted dynamically. Experimental data shows that during the period of rising carbon prices, the revenue of thermal power entities increases by 22% due to carbon quota trading, but the basic Shapley value method can only reflect 45% of the incremental contribution, resulting in a deviation of 12% between the distribution result and the actual contribution.

[0006] 4. Homogenized treatment of heterogeneous contributions: The output uncertainties of wind power and photovoltaic (the highest curtailment rate reaches 18%) and the peak shaving support role of thermal power (providing 15%-25% of the reserve capacity) are homogenized in the traditional model. This distribution method underestimates the revenue of thermal power entities assuming the responsibility of system flexibility by about 20%, while overestimates the revenue of new energy entities with high volatility by 15%-18%.

[0007] The above defects lead to an obvious interest imbalance within the alliance: the probability of pumped storage entities withdrawing from cooperation due to insufficient cost compensation increases by 40%, the revenue of thermal power enterprises decreases by 35% during the period of falling carbon prices, and the participation willingness of new energy entities decreases by 25% after the withdrawal of policy subsidies. Summary of the Invention

[0008] The object of the present invention is to provide a multi - energy alliance revenue - risk collaborative distribution system based on the improved Shapley value method to solve the above - mentioned technical problems.

[0009] To achieve the above object, the present invention provides a multi - energy alliance revenue - risk collaborative distribution system based on the improved Shapley value method, including:

[0010] A multi - dimensional contribution degree calculation module: used to calculate the low - carbon contribution degree, comprehensive revenue contribution degree, and cost contribution degree respectively;

[0011] A risk assessment and correction module: used to calculate the risk coefficient based on the entropy weight method, dynamically correct the Shapley value, and obtain the corrected Shapley value, that is, the alliance revenue distribution result;

[0012] A dynamic adjustment module: used to perform the redistribution of the Shapley value considering the comprehensive contribution degrees of different power generation entities, obtain the redistribution result, and adjust the redistribution result according to the difference between the comprehensive contribution degree and the average contribution degree of all power generation entities;

[0013] A data interface module: used to support data interaction with the power market, carbon trading market, and green certificate trading market.

[0014] Preferably, the multi - dimensional contribution degree calculation module includes a low - carbon contribution degree calculation unit, a comprehensive revenue contribution degree calculation unit, and a cost contribution degree calculation unit to calculate the low - carbon contribution degree, comprehensive revenue contribution degree, and cost contribution degree respectively;

[0015] Among them, the calculation formula for the low - carbon contribution degree is as follows:

[0016] χ i =χ thermal +χ new (1);

[0017]

[0018] In the formula, χ i is the low - carbon contribution degree of the i - th power generation entity; χ thermal and χ new are the low - carbon contribution degrees of thermal power plants and new - energy power plants respectively; F car is the carbon revenue generated by the surplus carbon quota of thermal power plants through the carbon trading market; F gre is the green certificate revenue;

[0019] The calculation formula for the comprehensive revenue contribution degree is as follows:

[0020]

[0021] In the formula, χ s,i is the comprehensive revenue contribution degree of the i - th power generation entity; Fs,i is the revenue of the \(i\)-th power generation entity; \(F\) all is the total revenue of the alliance;

[0022] The cost contribution degree \(\tau\) i The calculation formula is as follows:

[0023]

[0024] In the formula, \(\Delta C\) i is the cost change generated by the \(i\)-th power generation entity due to the change in the operating state; \(n\) is the number of power generation entities in the alliance.

[0025] Preferably, in the risk assessment and correction module, the risk coefficient calculation formula is as follows:

[0026]

[0027] Among them,

[0028]

[0029] In the formula, \(\rho\) i is the risk coefficient of the \(i\)-th power generation entity; \(\omega\) m is the entropy weight of the \(m\)-th risk index; \(y\) im is the value of the \(m\)-th risk index of the \(i\)-th power generation entity after standardization; \(M\) is the total number of risk indices; \(e\) m is the entropy value of the \(m\)-th risk index, and \(p\) im is the contribution ratio of the \(i\)-th power generation entity under the \(m\)-th risk index, \(k\) is a constant; \(x\) im is the value of the \(m\)-th risk index of the \(i\)-th power generation entity; \(\max\) 1≤i≤n \((x\) im ) and \(\min\) 1≤i≤n \((x\) im ) are the maximum and minimum values of the \(m\)-th risk index among \(n\) power generation entities respectively;

[0030] The dynamic correction formula for the Shapley value is as follows:

[0031]

[0032] Among them,

[0033]

[0034] In the formula, is the corrected Shapley value of the \(i\)-th power generation entity, that is, the share that the \(i\)-th power generation entity should receive from the total revenue of the alliance after considering the risk coefficient; is the Shapley value before correction for the i-th power generation entity; s i is all subsets containing the power generation entity i; | s i | is the number of elements in the subset s; ω(|s|) is the weighting factor in the subset s; v(s) is the revenue of the subset s; v(s / i) is the revenue obtained after removing the power generation entity i from the subset s.

[0035] Preferably, the risk indicators include carbon price volatility, green certificate demand elasticity, peak-valley difference of electricity price, wind and light curtailment rate, and equipment failure rate.

[0036] Preferably, in the dynamic adjustment module, the Shapley value redistribution equation considering the comprehensive contribution degree of different power generation entities is as follows:

[0037]

[0038] Among them,

[0039]

[0040] λ i =(ω1, ω2, ω3)(χ i , χ s,i , τ i ) T (13);

[0041] In the formula, is the Shapley value redistributed according to the contribution degree of the power generation entity i, that is, the redistribution result; M i is the difference between the comprehensive contribution degree of the power generation entity i and the average contribution degree of all power generation entities; F all is the total revenue of the alliance; λ i is the revenue distribution correction factor of the i-th power generation entity; ω1, ω2, and ω3 are the weighting factors of the low-carbon contribution degree, comprehensive revenue contribution degree, and cost contribution degree respectively, and ω1 + ω2 + ω3 = 1;

[0042] If M i > 0, then increase the redistribution revenue of the power generation entity i Otherwise, reduce the redistribution revenue of the power generation entity i

[0043] Therefore, the multi-energy alliance revenue-risk collaborative distribution system based on the improved Shapley value method adopted by the present invention has the following beneficial effects:

[0044] 1. A three-dimensional evaluation system including low-carbon contribution degree, comprehensive income contribution degree, and cost contribution degree is constructed, breaking through the limitation of the traditional Shapley value method that only relies on economic benefits. At the same time, by introducing the entropy weight method to quantify risk indicators and incorporating the risk coefficient into the distribution model, "income-risk" two-dimensional optimization is achieved;

[0045] 2. By realizing "rewarding the excellent and punishing the inferior" through the comprehensive contribution degree difference, the alliance cooperation period is extended from 12 months to 19 months.

[0046] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Description of the Drawings

[0047] Figure 1 It is a principle block diagram of the multi-energy alliance income-risk collaborative distribution system based on the improved Shapley value method of the present invention;

[0048] Figure 2 It is the income diagram of each subject for the simulation verification of the present invention. Detailed Embodiments

[0049] In order to make the purpose, technical solution, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described here 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 of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this application. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions throughout.

[0050] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units does not necessarily have to be 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.

[0051] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0052] The comprehensive income of the consortium not only includes the low-carbon income obtained by participating in the carbon market and the green certificate market, but also includes the electricity income obtained by participating in the electricity market. The contributions of different power generation entities to the comprehensive income of the consortium vary. Therefore, equations for the low-carbon contribution degree, comprehensive income contribution degree, and cost contribution degree of different entities are designed, and an improved method for allocating consortium income based on the Shapley value is constructed, laying a foundation for the stable operation of consortium cooperation.

[0053] Based on the above analysis, the present invention is designed as follows: As Figure 1 shown, a multi-energy consortium income-risk collaborative allocation system based on the improved Shapley value method includes: a multi-dimensional contribution degree calculation module: used to calculate the low-carbon contribution degree, comprehensive income contribution degree, and cost contribution degree respectively; a risk assessment and correction module: used to calculate the risk coefficient based on the entropy weight method, dynamically correct the Shapley value, and obtain the corrected Shapley value, that is, the consortium income allocation result; a dynamic adjustment module: used to perform the reallocation of the Shapley value considering the comprehensive contribution degree of different power generation entities, obtain the reallocation result, and adjust the reallocation result according to the difference between the comprehensive contribution degree and the average contribution degree of all power generation entities; a data interface module: used to support data interaction with the electricity market, carbon trading market, and green certificate trading market.

[0054] The multi-dimensional contribution degree calculation module includes a low-carbon contribution degree calculation unit, a comprehensive income contribution degree calculation unit, and a cost contribution degree calculation unit to calculate the low-carbon contribution degree, comprehensive income contribution degree, and cost contribution degree respectively;

[0055] Among them, the calculation formula for the low-carbon contribution degree is as follows:

[0056] χ i =χ thermal +χ new (1);

[0057]

[0058] In the formula, χ i is the low-carbon contribution degree of the i-th power generation entity; χ thermal and χ new are the low-carbon contribution degrees of thermal power plants and new energy power plants respectively; F car is the carbon income generated by the surplus carbon quota of thermal power plants through the carbon trading market; F gre is the green certificate income;

[0059] The calculation formula for the comprehensive income contribution degree is as follows:

[0060]

[0061] In the formula, χ s,iis the comprehensive revenue contribution degree of the i-th power generation entity; F s,i is the revenue of the i-th power generation entity; F all is the total revenue of the alliance;

[0062] The cost contribution degree τ i The calculation formula is as follows:

[0063]

[0064] In the formula, ΔC i is the cost change generated by the i-th power generation entity due to the change in the operating state; n is the number of power generation entities in the alliance.

[0065] And since the thermal power plant provides the basic power output and at the same time promotes the increase in the consumption of new energy, its marginal cost decreases. Therefore, neither the thermal power plant nor the new energy power plant has a cost loss; while the hydropower plant has a cost loss when providing standby auxiliary services. The cost contribution degree of the hydropower plant is included in the alliance revenue distribution process. Since only the hydropower has a cost loss, its cost contribution degree value is 1.

[0066] In the risk assessment and correction module, the risk coefficient calculation formula is as follows:

[0067]

[0068] Among them,

[0069]

[0070] In the formula, ρ i is the risk coefficient of the i-th power generation entity; ω m is the entropy weight of the m-th risk index; y im is the value of the m-th risk index of the i-th power generation entity after standardization; M is the total number of risk indices; e m is the entropy value of the m-th risk index, and p im is the contribution ratio of the i-th power generation entity under the m-th risk index, k is a constant; x im is the value of the m-th risk index of the i-th power generation entity; max 1≤i≤n (x im ) and min 1≤i≤n (x im ) are the maximum and minimum values of the m-th risk index among n power generation entities respectively;

[0071] The dynamic correction formula for the Shapley value is as follows:

[0072]

[0073] Among them,

[0074]

[0075] In the formula, is the corrected Shapley value of the i-th power generation entity, that is, the share that the i-th power generation entity should receive from the total income of the alliance after considering the risk coefficient; is the Shapley value of the i-th power generation entity before correction; s i is all subsets containing the power generation entity i; | s i | is the number of elements in the subset s; ω(|s|) is the weighting factor in the subset s; v(s) is the income of the subset s; v(s / i) is the income obtained after removing the power generation entity i from the subset s.

[0076] The risk indicators include carbon price volatility, green certificate demand elasticity, electricity price peak-valley difference, wind and light curtailment rate, and equipment failure rate.

[0077] In the dynamic adjustment module, the Shapley value redistribution equation considering the comprehensive contribution degree of different power generation entities is as follows:

[0078]

[0079] Among them,

[0080]

[0081] λ i =(ω1, ω2, ω3)(χ i , χ s,i , τ i ) T (13);

[0082] In the formula, is the Shapley value redistributed by the power generation entity i according to the contribution degree, that is, the redistribution result; M i is the difference between the comprehensive contribution degree of the power generation entity i and the average contribution degree of all power generation entities; F all is the total income of the alliance; λ i is the income distribution correction factor of the i-th power generation entity; ω1, ω2, and ω3 are the weighting factors of the low-carbon contribution degree, comprehensive income contribution degree, and cost contribution degree respectively, and ω1 + ω2 + ω3 = 1;

[0083] If M i > 0, then increase the redistribution income of the power generation entity i Otherwise, reduce the redistribution income of the power generation entity i

[0084] Simulation verification

[0085] System configuration: The IEEE 14-node system is adopted, and it is assumed that a total of 8 power generation entities are connected to the IEEE 14-node system. Among them, the 1st thermal power plant and the 2nd thermal power plant are connected to node 2, the 1st wind power plant and the 2nd wind power plant are connected to node 4, the 1st photovoltaic power station and the 2nd photovoltaic power station are connected to node 8, the pumped-storage power station is connected to node 11, and the 3rd photovoltaic power station is connected to node 14. The multi-energy alliance operation mode is adopted by the 1st and 2nd thermal power plants, the 1st wind power plant, the 1st photovoltaic power station and the pumped-storage power station.

[0086] Parameter settings: The carbon emission calculation coefficients a, b, and v are 36, -0.38, and 0.0034 respectively. Parameters of the pumped-storage power station: The maximum pumping power / generation power are 200 MW / 150 MW respectively, and the maximum pumping efficiency / generation efficiency are 85% / 80% respectively.

[0087] Table 1 Shapley contribution

[0088]

[0089] As Figure 2 shown, the average revenue and peak revenue of pumped-storage are significantly higher than those of other energy types, but its volatility is also the highest, with a standard deviation of about 210,000 yuan. It shows that the revenue of pumped-storage is more prominent during high electricity price periods, but its revenue is also easily affected by demand fluctuations. Although the revenues of wind power and photovoltaic are slightly lower than that of pumped-storage, their stability is stronger, reflecting the synergy effect among renewable energy sources. And the overall revenue of thermal power units is lower, especially for the 2nd thermal power, whose average revenue is the lowest. It shows that thermal power faces greater pressure in the changing power market, its revenue dependence is stronger, and its market position has been weakened under the background of the gradual penetration of renewable energy. It shows that the present invention can reasonably allocate the revenues of each power generation entity and maintain the participation enthusiasm of alliance members under different market mechanisms, verifying the applicability and balance of the present invention in the multi-energy alliance.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify or equivalently replace the technical solutions of the present invention, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A multi-energy alliance revenue-risk collaborative allocation system based on the improved Shapley value method, characterized in that: Including: Multi-dimensional contribution calculation module: used to calculate the low-carbon contribution, comprehensive income contribution, and cost contribution respectively; Risk assessment and correction module: used to calculate the risk coefficient based on the entropy weight method, dynamically correct the Shapley value, and obtain the corrected Shapley value, which is the preliminary income distribution result of the alliance; Dynamic adjustment module: used to perform the redistribution of the Shapley value considering the comprehensive contributions of different power generation entities, obtain the redistribution result, and adjust the redistribution result according to the difference between the comprehensive contribution and the average contribution of all power generation entities; Data interface module: used to support data interaction with the power market, carbon trading market, and green certificate trading market.

2. The multi-energy alliance revenue-risk collaborative allocation system based on the improved Shapley value method according to claim 1, characterized in that: The multi-dimensional contribution calculation module includes a low-carbon contribution calculation unit, a comprehensive income contribution calculation unit, and a cost contribution calculation unit to calculate the low-carbon contribution, comprehensive income contribution, and cost contribution respectively; The calculation formula for the low-carbon contribution is as follows: χ i = χ thermal + χ new (1); where χ i is the low-carbon contribution degree of the i-th power generation entity; χ thermal and χ new are the low-carbon contribution degrees of thermal power plants and new energy power plants respectively; F car is the carbon revenue generated by the surplus carbon quota of the thermal power plant through the carbon trading market; F gre is the green certificate revenue; The calculation formula for the comprehensive income contribution is as follows: where χ s,i is the comprehensive income contribution degree of the i-th power generation entity; F s,i is the income of the i-th power generation entity; F all is the total income of the alliance; Cost contribution degree τ i The calculation formula is as follows: where ΔC i is the cost change generated by the i-th power generation entity due to the change in its operating state; n is the number of power generation entities in the alliance.

3. The multi-energy alliance revenue-risk collaborative allocation system based on the improved Shapley value method according to claim 2, characterized in that: In the risk assessment and correction module, the risk coefficient calculation formula is as follows: Among them, Where ρ i is the risk coefficient of the \(i\)-th power generation entity; ω m is the entropy weight of the \(m\)-th risk index; y im is the value of the \(m\)-th risk index of the \(i\)-th power generation entity after standardization; M is the total number of risk indices; e m is the entropy value of the \(m\)-th risk index, and p im is the contribution ratio of the \(i\)-th power generation entity under the \(m\)-th risk index, k is a constant; x im is the value of the \(m\)-th risk index of the \(i\)-th power generation entity; max 1≤i≤n (x im ) and min 1≤i≤n (x im ) are the maximum and minimum values of the \(m\)-th risk index among \(n\) power generation entities respectively; The dynamic correction formula for the Shapley value is as follows: Among them, In the formula, is the corrected Shapley value of the i-th power generation entity, that is, the share that the i-th power generation entity should receive from the total income of the alliance after considering the risk coefficient; is the Shapley value of the i-th power generation entity before correction; s i is all subsets containing the power generation entity i; |s i | is the number of elements in the subset s; ω(|s|) is the weighting factor in the subset s; v(s) is the income of the subset s; v(s / i) is the income obtained after removing the power generation entity i from the subset s.

4. The multi-energy alliance revenue-risk collaborative allocation system based on the improved Shapley value method according to claim 3, characterized in that: The risk indicators include carbon price volatility, green certificate demand elasticity, electricity price peak-valley difference, curtailment of wind and light rate, and equipment failure rate.

5. The multi-energy alliance revenue-risk collaborative allocation system based on the improved Shapley value method according to claim 3, characterized in that: In the dynamic adjustment module, the Shapley value redistribution equation considering the comprehensive contributions of different power generation entities is as follows: Among them, λ i = (ω1, ω2, ω3,)(χ i , χ s,i , τ i ) T (13); In the formula, is the Shapley value redistributed by the power generation entity i according to the contribution degree, that is, the redistribution result; M i is the difference between the comprehensive contribution degree of the power generation entity i and the average contribution degree of all power generation entities; F all is the total income of the alliance; λ i is the income distribution correction factor of the i-th power generation entity; ω1, ω2, and ω3 are the weighted factors of the low-carbon contribution degree, the comprehensive income contribution degree, and the cost contribution degree, respectively, and ω1 + ω2 + ω3 = 1; If M i > 0, then increase the redistribution income of power generation entity i Otherwise, reduce the redistribution income of power generation entity i

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