Energy quota distribution and double-layer decision relation method based on fairness

Abstract

The invention discloses an energy quota distribution and double-layer decision relation method based on fairness. The method belongs to the technical field of energy policies and planning. And constructing a fairness measurement index system containing responsibility, capability and equality principles, and determining regional comprehensive scores and fairness constraints by integrating multiple data. A decision maker sets an objective function and constraint conditions by taking maximization of the renewable energy proportion under fairness and cost constraints as an objective; and an executor aims at minimizing the implementation cost and defines related constraints. An executor formulates an energy production transaction plan, a decision maker formulates a distribution target, interactive evaluation and adjustment are performed, and multiple times of loop optimization are performed, so that a reasonable quota distribution scheme is finally determined, and the targets of the decision maker and the executor in renewable energy quota distribution and implementation are coordinated.

Description

Technical Field

[0001] The present invention relates to the technical field of energy policies and planning, and particularly relates to a method for energy quota allocation and two-layer decision-making relationship based on fairness. Background Art

[0002] Renewable Portfolio Standard (RPS) has been applied in many countries, and the system for ensuring the consumption of renewable energy has been implemented in China. In the implementation of the RPS policy, reasonably setting the regional proportion target is the key, and two technical problems need to be solved: constructing a fairness evaluation index system and constructing an interaction relationship model between decision-makers and implementers. Disadvantages of the prior art: First, the existing allocation schemes are mainly based on the resource endowments and renewable energy investment costs of different regions, ignoring the economic differences between regions and not fully considering the fairness of allocation. Second, the existing quota system lacks a comprehensive optimization method that takes into account the interaction relationship between decision-makers and implementers, quantifies the allocation fairness of different regions, and takes into account the implementation economy of different regions. Summary of the Invention

[0003] The purpose of the present invention is to overcome one or more deficiencies of the prior art and provide a method for energy quota allocation and two-layer decision-making relationship based on fairness.

[0004] The purpose of the present invention is achieved through the following technical solutions:

[0005] A method for energy quota allocation and two-layer decision-making relationship based on fairness, the steps include:

[0006] Constructing a fairness measurement index: calculating the comprehensive score of the region based on the principles of responsibility, ability, and equality, and setting fairness constraints; the principles of responsibility, ability, and equality are to consider the historical energy utilization rate, regional per capita GDP, and the annual average concentration of fine particulate matter in the region;

[0007] Constructing the decision-maker's objective function and constraints: constraining to maximize the allocation ratio of renewable energy;

[0008] Constructing the implementer's objective function and constraints: calculating to minimize the cost of implementing the RPS; subject to constraints such as renewable energy quotas, electricity demand, power generation capacity, and power transmission;

[0009] The implementer obtains energy production transaction data;

[0010] The decision-maker determines the RPS allocation target according to the decision-making order and quota target;

[0011] The decision-maker calculates the quota target for the implementer, the implementer calculates the execution rate and plan and gives feedback, and the decision-maker evaluates and adjusts, repeating until the result is stable, for determining the final quota plan.

[0012] Furthermore, the fairness measurement index is expressed as:

[0013] ;

[0014] wherein, is the comprehensive score of region , is determined by , and is the normalized value of the measurement index, is the index of the measurement index, = 1, 2, 3 are the historical energy utilization rate, the per capita GDP of the region, and the annual average concentration of fine particulate matter in the region, respectively.

[0015] Furthermore, the decision-maker's goal is to maximize the proportion of renewable energy allocated under the constraints of the fairness measurement index and the total regional power generation cost. The objective function is:

[0016] ;

[0017] wherein, represents the target value of energy utilization allocated by the decision-maker to region , is the execution rate of the energy utilization target for region , represents the electricity demand of region .

[0018] Furthermore, the renewable energy quota ratio needs to satisfy:

[0019] ;

[0020] ;

[0021] wherein, represents the current renewable energy utilization ratio of region ; represents the maximum renewable energy utilization ratio allowed by the existing resource conditions of region ; The quota allocation among different regions needs to comply with the fairness constraint:

[0022] ;

[0023] wherein, is the fairness parameter, .

[0024] Furthermore, the implementer needs to minimize the cost of implementing the RPS. Its objective function includes the cost of power generation technologies and the cost of inter-regional power transactions; the constraint conditions include the renewable energy quota constraint:

[0025] ;

[0026] ;

[0027] Among them, among them represents the power generation capacity of renewable energy r in region j, is a decision variable, represents the annual available hours of renewable energy r in region j, is a 0-1 variable, and 1 means region can transmit electricity to region , and 0 means region cannot transmit electricity to region , represents the proportion of renewable energy exported from region to region , that is, the symmetric matrix represents the proportion of renewable energy imported from region to region ; represents the loss rate of long-distance power transmission; and represent the lower and upper limits of the execution rate, which are determined by the decision maker; is the amount of electricity imported from region to region , is for exported to region amount of electricity.

[0028] Furthermore, the decision maker constructs an RPS allocation target with fairness as the constraint condition and maximizing the global clean energy proportion as the goal.

[0029] Furthermore, initially set the execution ratio to 1, assuming that each region can fully execute the renewable energy quota target; the decision maker calculates the optimal renewable energy quota target obtained by considering the fairness measurement index, and then calls to the executor.

[0030] The beneficial effects of the present invention are:

[0031] (1) Adopt an interactive quota allocation method of fairness and economy, effectively solve the problems of fairness and economy, construct a fairness measurement index, dynamically adjust the quota, and improve the fairness and rationality of allocation;

[0032] (2) The method is highly flexible and adaptable. In implementation, the implementers communicate and coordinate, and the quota plan can be adjusted according to the actual situation of the region to promote the rational utilization of renewable energy. Description of the Drawings

[0033] Figure 1 It is a flow chart of the steps of the energy quota allocation and two - layer decision - making relationship method based on fairness;

[0034] Figure 2 It is a two - layer interaction relationship diagram between decision - makers and implementers in PRS quota allocation. Specific Embodiments

[0035] The technical solutions of the present invention will be described clearly and completely below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art without creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0036] Embodiment 1:

[0037] Refer to Figure 1 A method for energy quota allocation and two - layer decision - making relationship based on fairness, the steps of the method include:

[0038] Construct a fairness - based measurement index: Calculate the comprehensive score of the region based on the principles of responsibility, ability, and equality, and set fairness constraints; the principles of responsibility, ability, and equality are to consider the historical energy utilization rate, regional per capita GDP, and the annual average concentration of fine particulate matter in the region;

[0039] Construct the decision - maker's objective function and constraints: Maximize the allocation ratio of renewable energy under constraints;

[0040] Construct the implementer's objective function and constraints: Calculate the minimum cost of implementing RPS; subject to constraints such as renewable energy quota, electricity demand, power generation capacity, and power transmission;

[0041] The implementer obtains energy production transaction data;

[0042] The decision - maker determines the RPS allocation target according to the decision sequence and quota target;

[0043] The decision - maker calculates the quota target and gives it to the implementer. The implementer calculates the execution rate and plan and gives feedback. The decision - maker evaluates and adjusts, and loops until the result is stable to determine the final quota plan.

[0044] Embodiment 2:

[0045] The renewable energy quota target allocated to each region and the implemented renewable energy quota target satisfy the following constraints:

[0046] ;

[0047] ;

[0048] In the embodiment, the power generation technologies available in each region are classified into renewable energy power generation and non-renewable energy power generation. Among them, wind energy, solar energy, biomass energy, and hydropower are classified as renewable energy power generation technologies, and coal combustion thermal power, natural gas, and nuclear energy are classified as non-renewable energy power generation technologies. The expression of the objective function is as follows:

[0049] ;

[0050] In the embodiment, after the renewable energy quota is allocated the executor determines its execution rate according to the local resource situation and the balance between the supply and demand of renewable energy ; represents the power generation capacity of renewable energy r in region j, which is a decision variable, represents the annual available hours of renewable energy r in region j, is a 0-1 variable, 1 means region can transmit electricity to region , 0 means region cannot transmit electricity to region represents the proportion of renewable energy exported from region to region , that is, the symmetric matrix represents the proportion of renewable energy imported from region to region ; represents the loss rate of long-distance power transmission; and represent the lower and upper limits of the execution rate, which are determined by the decision maker; is the amount of electricity imported from region to region , is the amount of electricity exported from to region . The renewable energy quota constraint is as follows:

[0051] ;

[0052] ;

[0053] The basic constraint of the power plant is that it must meet the local demand, which can be expressed as follows:

[0054] ;

[0055] ;

[0056] ;

[0057] ;

[0058] ;

[0059] wherein, and are respectively the power generation amounts in the previous decision cycle (the previous year of the upcoming decision cycle), and are respectively the lower limit and upper limit parameters, represents the region and the upper transmission limit between them.

[0060] Embodiment 3:

[0061] Refer to Figure 2 , (1) Construct an fairness measurement index system:

[0062] Collect the data of renewable energy power generation amounts and total power generation amounts in each region in the recent 3 years, and calculate the historical renewable energy utilization rate (responsibility principle). For example, the total renewable energy power generation amount in Region A in the past 3 years is 60 billion kWh, and the total power generation amount is 300 billion kWh, and its historical renewable energy utilization rate is 20%. At the same time, obtain the data of the per capita GDP (ability principle) and the annual average PM2.5 concentration (equality principle) in each region in the previous year.

[0063] Calculate the normalized values of each principle. Assume that there are 5 regions in the country. Calculate that the normalized value of the responsibility principle in Region A is 0.2 (the calculation method is the historical renewable energy utilization rate in Region A divided by the sum of this value in each region in the country). For the per capita GDP, if the national average per capita GDP is 80,000 yuan and the per capita GDP in Region A is 100,000 yuan, calculate the normalized value of the ability principle (the calculation method is to divide the result obtained by subtracting the ratio of the per capita GDP in Region A to the national average value from 1 and then dividing by the sum of the calculation results in each region in the country). The annual average PM2.5 concentration is calculated in the same way. For example, if the annual average PM2.5 concentration in Region A is relatively high, its normalized value of the equality principle is 0.3 (the calculation is the concentration value in Region A divided by the sum of the concentrations in each region in the country).

[0064] Set the weights of responsibility, ability, and equality to be 0.3, 0.3, and 0.4 respectively, and calculate the comprehensive score. For example, the comprehensive score of Region A is 0.3×0.2 + 0.3×(1 - 10 / 8) + 0.4×0.3 = 0.21 (in the calculation process, the part 1 - 10 / 8 in the calculation of the ability principle is the intermediate step for calculating the normalized value according to the per capita GDP). Set the fairness parameter α = 0.85 to determine the fairness constraint range.

[0065] (2) Determine the decision-maker's objective function and constraints:

[0066] Objective function and constraint setting:

[0067] The goal of the decision-maker is to maximize the proportion of renewable energy allocation under the constraints of fairness and cost. The objective function is:

[0068] ;

[0069] Assume that the electricity demand in 5 regions is 10 billion kWh, 12 billion kWh, 8 billion kWh, 15 billion kWh, and 9 billion kWh respectively ( value), and initially set the renewable energy utilization targets for each region (initial assumed value, which can be adjusted later), for example, set region A to 0.25, and the implementation rate is temporarily set to 1, and the initial value can be calculated .

[0070] The quota target for each region satisfies , and the quota allocation among different regions needs to meet the fairness constraint:

[0071] .

[0072] (3) Define the executor's objective function and constraints:

[0073] Objective function construction and constraint definition:

[0074] The executor aims to minimize the cost of implementing the RPS, including various power generation costs and transaction costs. For example, in a certain region, there is solar power, wind power (renewable energy) and coal power (non-renewable energy). The cost of solar power generation is 0.7 yuan per kWh, wind power is 0.6 yuan, and coal power is 0.4 yuan. Considering the power generation capacity (such as solar power generation capacity , wind power , coal power ) and factors such as power generation time to construct the objective function.

[0075] Renewable energy quota constraint. Assume that the allocated quota for region A = 0.25, the implementation rate = 0.8, and the electricity demand = 10 billion kWh. Determine the satisfaction of the constraint according to the actual power generation capacity, transmission ratio (such as the proportion of imported renewable energy from neighboring regions in the total demand is assumed to be 0.1), power transmission loss rate (assumed to be 5%), etc. The upper and lower limits of the implementation rate are assumed to be 0.6 to 1 and need to be within the range. The electricity demand constraint ensures that the power generation and trading plans meet the local demand. The power generation capacity is based on the previous year's power generation (such as the previous year's solar power generation = 4 billion kWh) and upper and lower limit parameters (assuming the lower limit parameter = 0.7) are satisfied. For the inter-regional power transmission constraint, assume the upper limit of transmission to neighboring regions is 3 billion kWh, and the import and export electricity volume needs to be within the range.

[0076] (4) The executor formulates the energy production and trading plan:

[0077] Decision-making order and plan formulation:

[0078] Determine the decision-making order based on power transmission. Regions with rich resources and export potential make decisions first, and set the upper limit of the seller's transmission. For example, Region A is rich in renewable energy. According to the historical power generation and trading situation, set the upper limit of transmission to other regions within this planning period to 2 billion kWh and inform other regions.

[0079] Region A combines the quotas allocated by the decision-maker = 0.25, and determines the execution rate according to local resources (such as sufficient solar energy resources, evaluate the additional solar power generation capacity that can be added) and supply-demand balance = 0.8, and then determines the power generation capacity (increase the solar power generation capacity ) and the trading electricity volume (export electricity volume to other regions ) plan to minimize the cost.

[0080] (5) The decision-maker formulates the RPS allocation target:

[0081] Target adjustment:

[0082] The decision-maker constructs the allocation target with fairness and maximizing global clean energy as the goal. After receiving the feedback from the executor, if it is found that the execution rate of Region B is low but there is room for improvement, and the execution situation of Region C is good and resources permit, appropriately adjust the quota target. For example, adjust Region B from 0.2 to 0.23, and Region C from 0.18 to 0.21, and then re-allocate to each region.

[0083] (6) Interactive evaluation and adjustment by the executor:

[0084] Interactive evaluation:

[0085] Set the initial execution rate to 1, and the decision-maker calculates the quota target and allocates it to the executor. The executor calculates the execution rate and the plan based on the new and gives feedback. After the first cycle, for example, the execution rate of Region A becomes 0.75 and is fed back to the decision-maker. The decision-maker re-evaluates based on the new If it does not reach the optimal or stable state, continue to adjust After multiple rounds of iteration (assuming 3 - 4 rounds), if the implementation rates and quota targets in each region change slightly in the last round, the final renewable energy quota allocation plan is determined. For example, if the quota target for Region A is determined to be 0.3 and the implementation rate is stable at 0.85, etc., it realizes the coordinated optimization between decision - makers and implementers in the allocation and implementation of RPS targets.

[0086] Example 4:

[0087] 1. Construct an index system for fairness measurement:

[0088] Data collection and preliminary calculation: Obtain the renewable energy generation data and total generation data of Regions A, B, and C in the past 3 years from authoritative departments, and calculate the historical renewable energy utilization rate. For example, if the total renewable energy generation in Region A in the past 3 years is 30 billion kWh and the total generation is 150 billion kWh, its historical renewable energy utilization rate is 20%. At the same time, obtain the per capita GDP and annual average PM2.5 concentration data of each region in the previous year. Assume that the per capita GDP of Region B is 90,000 yuan and the national average per capita GDP is 70,000 yuan; the annual average PM2.5 concentration in Region C is 38 μg / m³ and the national average annual average PM2.5 concentration is 33 μg / m³.

[0089] Calculation of normalized values: For the responsibility principle, if the total historical renewable energy utilization rate of these three regions in the country is 60%, the normalized value of the historical renewable energy utilization rate in Region A is 20÷60≈0.33. For the ability principle, the normalized value calculation of Region B for the ability principle is (assuming the sum of this part of these three regions in the country is 3), and the result is approximately - 0.29÷3≈ - 0.097. For the equality principle, the normalized value of Region C for the equality principle is 38÷(33×3)≈0.38 (assuming only three regions participate in the calculation of the sum).

[0090] Determination of comprehensive scores and fairness constraints: Assume that the weights of responsibility, ability, and equality are 0.35, 0.3, and 0.35 respectively. The comprehensive score of Region A is 0.35×0.33 + 0.3×( - 0.097)+0.35×0.38≈0.23. Set the fairness parameter α = 0.92, then according to the fairness principle constraint, determine the relatively fair range of quota allocation for the three regions, which is: 。

[0091] 2. Determine the decision - maker's objective function and constraint conditions:

[0092] Construction of the objective function and example calculation: The decision - maker's goal is to maximize the renewable energy allocation ratio under fairness and cost constraints, and the objective function is:

[0093] ;

[0094] Among them, For the region 's comprehensive score, is the normalized value of the measurement indicators determined by , and . is the index of the measurement indicator, = 1, 2, 3 are the historical energy utilization rate, regional per capita GDP, and annual average concentration of fine particulate matter in the region respectively.

[0095] Assume that the electricity demands of three regions A, B, and C are respectively = 8 billion kWh, = 9 billion kWh, = 7 billion kWh. First, initially set the renewable energy utilization targets for each region, such as = 0.28, = 0.23, = 0.2, and the initial execution rate is set to 1 for all, and calculate the initial value:

[0096] ;

[0097] Get the value to be ≈ 0.238.

[0098] Constraint setting: The quota target of each region is met, and the quota allocation among different regions conforms to the fairness constraint calculated previously.

[0099] 3. Define the executor's objective function and constraints:

[0100] Objective function and cost constraint: The executor aims to minimize the cost of implementing the RPS. For example, in region A, there is solar power, wind power (renewable energy), and coal power (non-renewable energy). The cost of solar power generation is 0.75 yuan per kWh, wind power is 0.65 yuan, and coal power is 0.38 yuan. Considering factors such as power generation capacity (solar power generation capacity , wind power and coal power ) and power generation time, etc., construct the objective function, and at the same time consider the electricity trading cost with other regions (including the import and export electricity volume , and the power transmission loss rate , assumed to be 4%).

[0101] Constraint clarification:

[0102] Renewable energy quota constraint. If the allocated quota for region A = 0.28 and the execution rate = 0.85, the electricity demand = 8 billion kWh, which is determined to meet the constraints according to the actual power generation capacity, transmission ratio (assuming that the proportion of renewable energy imported from Region B in the total demand is 0.08), etc.

[0103] The upper and lower limits of the execution rate are assumed to be from 0.65 to 1, and the execution rate in Region A needs to be within this range.

[0104] Power demand constraint: It is necessary to ensure that the power generation and trading plans meet the local demand, which is:

[0105] ;

[0106] Power generation capacity constraint: According to the electricity generation in the previous year (such as the electricity generation of solar energy in the previous year = 3 billion kWh) and the upper and lower limit parameters (assuming the lower limit parameter = 0.7) to meet the constraints, which is:

[0107] .

[0108] Inter-regional power transmission constraint: Assume that the upper limit of power transmission between Region A and Region B is 2.5 billion kWh, and the imported and exported electricity needs to be within the range, that is and .

[0109] 4. The executor formulates the energy production and trading plan:

[0110] Decision-making order and plan formulation: Determine the decision-making order based on power transmission. Assume that Region A is rich in renewable energy and has the potential to export, so it makes decisions first. According to historical situations, set the upper limits of power transmission to Region B and Region C during this planning period to be 1.8 billion kWh and 1.5 billion kWh respectively and inform.

[0111] Plan determination and cost optimization: Region A combines the quota allocated by the decision-maker = 0.28, and determines the execution rate according to local resources (such as good solar energy resources, and it is estimated that the solar power generation capacity can be increased) and supply-demand balance = 0.8, and then determines the power generation capacity (increase the solar power generation capacity and the trading electricity volume (the electricity volume exported to Region B and Region C , ) plan to minimize the cost.

[0112] 5. The decision-maker formulates the RPS allocation target:

[0113] Target adjustment: The decision-maker constructs the allocation target with fairness and maximizing the global clean energy as the goal. After receiving the feedback from the executor, if it is found that although the execution rate in Region B is low but the resources can be tapped, and Region C has good execution and potential, the quota target is appropriately adjusted, such as changing from 0.23 to 0.25, Adjust it from 0.2 to 0.22 and then re - allocate it to each region.

[0114] 6. Upper executor interactive evaluation and adjustment:

[0115] Interaction loop: Set the initial execution rate to 1, and the decision - maker calculates the quota target and allocate it to the executor. The executor calculates the execution rate according to the new and plan and give feedback. After the first loop, if the execution rate in Region A becomes 0.78, it is fed back to the decision - maker.

[0116] Scheme determination: The decision - maker re - evaluates according to the new situation. If it does not reach the optimal or stable state, continue to adjust . After multiple loops (assuming 3 - 4 times), if the execution rates and quota targets in each region change slightly in the last loop, determine the final renewable energy quota allocation scheme. For example, if the quota target in Region A is finally determined to be 0.3 and the execution rate stabilizes at 0.83, etc., the coordination and optimization between the decision - maker and the executor in the allocation and implementation of the RPS target are achieved.

[0117] By adopting the fairness - and - economy interactive quota allocation method, the fairness and economy problems are effectively solved, a fairness measurement index is constructed, the quota is dynamically adjusted, and the fairness and rationality of the allocation are improved; moreover, this method has strong flexibility and adaptability, realizes the communication and coordination between the upper and lower executors, can adjust the quota scheme according to the actual situation of the region, and promotes the rational utilization of renewable energy.

[0118] The above - mentioned is only the preferred implementation mode of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be changed within the scope of the concept described herein through the above teachings or the technology or knowledge in related fields. And the changes and alterations made by those skilled in the art that do not depart from the spirit and scope of the present invention shall fall within the protection scope of the appended claims of the present invention.

Claims

1. A method for energy quota allocation and two-level decision-making relationship based on fairness, characterized in that the steps include: Constructing fairness-based measurement indicators: Based on the principles of responsibility, ability and equality, calculate the comprehensive scores of regions and set fairness constraints; The responsibility, capability and equality principles mentioned are historical energy utilization rate, regional GDP per capita and regional annual average fine particulate matter concentration; Construct the decision maker's objective function and constraints: the constraint is to maximize the allocation ratio of renewable energy; Construct the executor's objective function and constraints: calculate and minimize the cost of implementing RPS; The executor obtains energy production transaction data; the decision maker determines the RPS allocation target based on the decision sequence and quota target; The decision maker calculates the quota target for the executor, the executor calculates the execution rate and plan and feeds it back to the decision maker, the decision maker evaluates and adjusts, and determines the final quota plan.

2. The method of energy quota allocation and two-level decision-making relationship based on fairness according to claim 1, characterized in that: The fairness measure is expressed as: ； in, For Region The comprehensive score of Is , and The normalized value of the determined metric, is the index of the metric, =1, 2, 3 are the historical energy utilization rate, regional per capita GDP and the average annual fine particulate matter concentration in the region, respectively.

3. The method of energy quota allocation and two-level decision-making relationship based on fairness according to claim 1, characterized in that: The decision maker's goal is to maximize the proportion of renewable energy quota allocated under the constraints of fairness measurement indicators and total regional power generation cost. The objective function is: ； in, Indicates the decision maker assigned to the region The energy utilization target value is For the region Energy utilization target execution rate, Indicates the area The amount of electricity demand.

4. The method of energy quota allocation and two-level decision-making relationship based on fairness according to claim 3 is characterized in that: The renewable energy quota ratio must meet the following requirements: ； ； in, Indicates the area The current proportion of renewable energy utilization; Indicates the area The maximum renewable energy utilization ratio allowed by existing resource conditions; the quota allocation between different regions must comply with fairness constraints: ； in is the fairness parameter, .

5. The method of energy quota allocation and two-level decision-making relationship based on fairness according to claim 1, characterized in that: The implementer needs to minimize the cost of implementing RPS. Its objective function includes the cost of power generation technology and the cost of inter-regional power trading. The constraints include renewable energy quota constraints: ； ； in, represents the power generation capacity of renewable energy r in region j, is the decision variable, represents the annual available hours of renewable energy r in region j, It is a 0-1 variable, 1 represents the region Capable of transmitting electricity to the region , 0 means region Unable to transmit electricity to the area , Indicates from the region Export to Region The proportion of renewable energy is the symmetric matrix Indicates from the region Import to Region the proportion of renewable energy in the It represents the loss rate of long-distance power transportation; and It represents the lower and upper limits of the execution rate, which are determined by the decision maker; For the region Import to Region of electricity, For Export to Region of electricity.

6. The method for allocating energy quotas and making decisions based on fairness according to claim 1, characterized in that: Decision makers use fairness as a constraint and construct RPS allocation targets with the goal of maximizing the global clean energy share.

7. The method of energy quota allocation and two-level decision-making relationship based on fairness according to claim 4, characterized in that: Initial execution ratio Set to 1, the decision maker calculates the optimal renewable energy quota target considering the fairness measurement index , then call To the executor.

Images