A multi-virtual power plant optimal operation and distribution method in carbon-electricity market

Abstract

The present application relates to a kind of carbon-electricity market in the optimal operation and distribution method of multiple virtual power plants, wherein the method includes: considering the ability of virtual power plant to participate in carbon asset management, establish the optimal model of multiple virtual power plant economic benefits considering risk and carbon flow;Establish a day-ahead optimization cooperation game model of multiple virtual power plants, solve the cooperation game model to obtain the economic benefits obtained by each virtual power plant in day-ahead operation and the power, carbon quota price and the adjustment amount of each adjustable unit that each virtual power plant trades with superior grid and directly trades with other virtual power plants in day-ahead;Comprehensive correction factor is introduced to quantify the influence of distribution factors on distribution results, an improved weighted Shapley value virtual power plant benefit distribution model is constructed, and the distribution results reflecting the multi-dimensional input and contribution level of each element in virtual power plant are obtained.Compared with the prior art, the present application has the advantages of high economic benefit, excellent environmental benefit, more fair and reasonable distribution results, etc.

Description

Technical Field

[0001] This invention relates to the field of virtual power plant technology, and in particular to a method for optimizing the operation and allocation of multiple virtual power plants in a carbon-electricity market. Background Technology

[0002] Virtual power plants (VPPs) combine distributed power sources, energy storage, and loads into a new market entity that can be routinely dispatched. In the electricity market, VPPs can cope with the random fluctuations in output and uncontrollable changes in load demand when DERs (Distributed Energy Resources) participate in the market alone. In the carbon trading market, VPPs can fully realize their environmental benefits. The optimized operation of virtual power plants in the carbon-electricity trading market has gradually attracted attention.

[0003] Existing literature has introduced carbon trading mechanisms into VPP bidding and established environmental and economic coordinated optimization scheduling models. However, these studies are all based on the strategy optimization of VPP as an independent entity that only interacts with the upper-level power grid, which has problems such as low flexibility in participation in scheduling and large risk losses. The Shapley value method is widely used for the benefit allocation of multi-entity cooperative games, but existing literature on the allocation scheme of internal components of virtual power plants has problems such as insufficient consideration of alliance operation risks, insufficient consideration of the sacrifice of internal component satisfaction, and failure to take into account the impact of carbon flow on the allocation results, which need further improvement.

[0004] Therefore, there is an urgent need to develop a method for optimizing the operation and allocation of multiple virtual power plants in the carbon-electricity market, so as to achieve optimal benefits for virtual power plants before the operating day and fair and reasonable distribution of benefits after the operating day. Summary of the Invention

[0005] The purpose of this invention is to provide a method for optimizing the operation and allocation of multiple virtual power plants in the carbon-electricity market, taking into account both economic and environmental benefits, and ensuring a more reasonable and fair allocation.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] A method for optimizing the operation and allocation of multiple virtual power plants in a carbon-electricity market includes the following steps:

[0008] Considering the ability of virtual power plants to participate in carbon asset management, establish an optimal economic benefit model for multiple virtual power plants that takes into account risk and carbon flow.

[0009] A multi-virtual power plant day-ahead optimization cooperative game model is established. The cooperative game model is solved to obtain the economic benefits obtained by each virtual power plant in day-ahead operation, as well as the electricity, carbon quota price and quantity traded by each virtual power plant with the upper-level grid and directly traded with other virtual power plants in day-ahead, and the adjustment amount of each adjustable unit.

[0010] By introducing a comprehensive correction factor to quantify the impact of allocation factors on the allocation results, an improved weighted Shapley value virtual power plant benefit allocation model is constructed, resulting in allocation results that reflect the multi-dimensional input and contribution levels of each component in the virtual power plant.

[0011] The economic benefits of virtual power plants include revenues from the electricity market and the carbon trading market:

[0012]

[0013] in, For the economic benefits of virtual power plant i at time t, For the revenue that virtual power plants obtain in the electricity market, This refers to the revenue that virtual power plants obtain in the carbon trading market.

[0014] The revenue that the virtual power plant obtains in the electricity market is as follows:

[0015]

[0016] In the formula: These are the benefits of direct power interaction of the virtual power plant i at time t, the grid access fee to be paid, the benefits of trading power with the main grid, the cost of calling up adjustable loads, the cost of calling up energy storage, and the operation and maintenance cost of DER.

[0017] Let t represent the direct transaction price and power between virtual power plant i and virtual power plant j at time t; Let t be the normal transaction price between the virtual power plant and the power grid; Let θ be the interrupt amount of the adjustable load k invoked at time t. k This is the parameter for the adjustable load type; Let be the charging and discharging power of the stored energy e at time t, where η e These correspond to the charging / discharging power and efficiency, respectively. Cost per unit of call; Let m be the power generation output of renewable energy at time t; The penalty electricity price for maintaining power balance when the virtual power plant cannot meet the output requirements at point PCC at time t is denoted as t. It is a 0-1 variable, which is 1 when the power demand is exceeded or insufficient, respectively; The penalties are for exceeding or falling short of demand. γ is the line loss conversion factor, N is the total number of virtual power plants participating in the cooperative alliance, and M is the total number of renewable power generation resources in the virtual power plants. K1 to K6 are positive parameters representing the deviation power that still cannot meet the power requirements of the PCC point after inter-VPP transactions and internal adjustments.

[0018] The revenue that the virtual power plant obtains in the carbon trading market is as follows:

[0019]

[0020] In the formula: These represent the benefits of direct carbon quota interaction with virtual power plant i at time t, and the benefits of trading carbon quotas with the main grid. Let t represent the direct trading carbon allowance price and the direct trading carbon allowance quantity between virtual power plant i and virtual power plant j. The market price of the carbon allowance traded between the virtual power plant and the main grid at time t;

[0021] Let represent the carbon allowance for virtual power plant i at time t. The responsibility for purchased electricity for virtual power plant i at time t.

[0022] As a new type of market participant, virtual power plants can improve their economic efficiency and fully realize their environmental benefits by participating in carbon trading. Carbon emission allowances and certified voluntary emission reductions (CCERs) are currently the two types of traded products in my country's carbon trading market. In this invention, the product traded in the carbon market is the carbon emission allowance. According to the baseline method, the carbon allowance can be described as:

[0023]

[0024] In the formula, The carbon emission factor per unit of electricity. Let m be the amount of electricity generated by renewable energy resource m in virtual power plant i at time t.

[0025] The electricity and carbon trading of virtual power plants are coupled, requiring the optimization of internal resources based on real-time dynamic demand to improve the overall profitability of carbon-electricity trading. As an independent market entity, the virtual power plant's energy supply units are wind and solar power generators. When the renewable energy output of the virtual power plant cannot meet the normal electricity demand, it needs to purchase electricity from the grid. In this case, in addition to incurring the cost of purchasing electricity, the virtual power plant also needs to bear the carbon emissions corresponding to the production of that electricity. The external electricity purchase responsibility of virtual power plant i at time t is described as follows:

[0026]

[0027] In the formula, τ is the proportion of non-clean energy power generation capacity in the regional power grid to the total installed capacity, and κ is the carbon emission responsibility factor corresponding to a unit of non-clean purchased electricity. The purchased electricity of VPP i refers to the deviation electricity that still cannot meet the power balance requirements after direct and internal adjustments between VPPs during the collaborative optimization operation of multiple virtual power plants.

[0028] The optimal operating objective of the multi-virtual power plant economics optimization model considering risk and carbon flow is:

[0029]

[0030] In the formula, For the benefit at time t of virtual power plant i participating in cooperative operation, β i The risk preference coefficient reflects the degree of risk preference of virtual power plant i. For the economic benefits of the virtual power plant i at time t, ρ CVaR,i Let i be the conditional risk value of virtual power plant i.

[0031] The constraints of the optimal economic model for multiple virtual power plants that considers risk and carbon flow include:

[0032] R1) Energy constraint:

[0033]

[0034]

[0035]

[0036] In the formula, Let i be the total direct interaction power of the virtual power plant at time t; These represent the total predicted DER output and load power consumption of the virtual power plant i at time t, as well as the agreed-upon interactive power at the PCC point.

[0037] R2) Electricity price constraints:

[0038]

[0039]

[0040] In the formula, Let $t$ be the unit grid connection cost when a direct transaction occurs between virtual power plant i and virtual power plant j at time $t$. Let t be the unit operation and maintenance cost of DER within the virtual power plant i at time t. Let be the total cost of calling up adjustable components within virtual power plant i at time t. The price at which the main grid sells carbon allowances to the virtual power plant at time t.

[0041] R3) Adjustable load constraint:

[0042]

[0043]

[0044] In the formula, The maximum number of calls is the adjustable load k. The state of the adjustable load k at time t is a 0 / 1 variable; T IL,kmax This represents the maximum interruption time.

[0045] R4) Energy storage constraints:

[0046]

[0047]

[0048]

[0049]

[0050]

[0051] In the formula, The state of charge of energy storage unit e during time period t; E is a 0 / 1 variable used to determine the discharge and charge states. e,N P ch,e,max P ch,emin P dis,e,max P dis,e,min S soc,e,max S soc,e,min These represent the rated capacity, charge / discharge power, and upper and lower limits of the state of charge of energy storage unit e, respectively.

[0052] R5) Carbon quota trading price and quantity constraints:

[0053] Each virtual power plant is only authorized to sell or purchase carbon allowances at any given time. The trading price and quantity constraints for carbon allowances include:

[0054]

[0055]

[0056]

[0057] In the formula, The price at which the main grid purchases carbon allowances from the virtual power plant at time t. c,s nc,s These represent virtual power plants with power purchase quotas and virtual power plants with power sales quotas, respectively.

[0058] The objective function of the current-day optimization cooperative game model for multiple virtual power plants is:

[0059]

[0060] In the formula, N represents the total number of virtual power plants participating in the cooperative alliance. Let be the benefits of virtual power plant i participating in cooperative operation and the optimal benefits of operating alone at time t, respectively. At that time, virtual power plant i will no longer participate in cooperative operation.

[0061] By transforming the cooperative game model using the mean inequality, the solution can obtain the economic benefits obtained by each VPP in day-ahead operation, as well as the electricity, carbon quota price and quantity traded by each VPP with the upstream grid and directly with other VPPs in day-ahead transactions, and the adjustment amount of each adjustable unit.

[0062] The improved weighted Shapley value virtual power plant benefit allocation model is as follows:

[0063]

[0064]

[0065] In the formula, This is the result of the allocation of the corrected element q; To correct the change in the economic benefits of component q before and after; The result is the allocation of element q before correction; C i For the overall economic benefits of virtual power plant i; ΔC q For the comprehensive correction factor, T represents the sub-alliance composed of some participants, x represents economic benefits, and v represents the weighting factor.

[0066] The allocation factors include risk, satisfaction, and carbon emission reduction contribution, and the corresponding correction factors include risk indicator factor, satisfaction factor, and carbon emission reduction contribution factor.

[0067] A comprehensive correction factor is constructed considering risk, satisfaction, and carbon emission reduction contributions. Since the indicators have different dimensions, they need to be preprocessed by normalization before a weighted average is applied to obtain the final allocation scheme. The calculation process for each correction factor is as follows:

[0068] 1) Risk indicator factors

[0069] Wind power, photovoltaic power, and conventional loads are defined as non-adjustable components, while adjustable loads and energy storage units are defined as adjustable components. These two types of components exhibit risk aversion and risk preference, respectively, in relation to prediction deviations. Based on utility theory, commonly used utility functions are selected and marginal values ​​are substituted to quantify the risk utility of each type of component.

[0070]

[0071]

[0072] In the formula, U(f) q1 f is a utility function with risk-averse components. q1 ={f PV ,f WT ,f LOAD} represents the risk adaptation function for non-adjustable components, which is related to prediction bias; This is the predicted value for non-adjustable components.

[0073]

[0074]

[0075] In the formula, U(f) q2 f is a utility function for components with risk preferences. q2 ={f IL ,f ess} is the risk adaptation function of the schedulable object, and its value is related to the maximum adjustable amount of the schedulable object. And it is related to the prediction accuracy of unschedulable objects.

[0076] The risk factors for each object are:

[0077]

[0078] 2) Satisfaction Factor

[0079] Among the various components within the VPP, only adjustable loads are considered to require a satisfaction factor; the satisfaction factors for other components are set to 0. The root mean square error (RMSE) is used to measure the difference in adjustable load curves before and after optimization, describing the degree of sacrifice in electricity user satisfaction. The satisfaction factors for each component are as follows:

[0080]

[0081]

[0082] In the formula, K is the set of adjustable loads. These represent the interruption amount of the adjustable load k at time t and the power consumption of the adjustable load before optimization, respectively.

[0083] 3) Carbon emission reduction contribution factors

[0084] Wind power, solar power, and various demand response resources are the main drivers of carbon emission reduction. Conversely, electricity-consuming load units are the main indirect contributors to carbon emissions. Based on this, a carbon emission reduction contribution model was established to analyze the contribution of each component within a VPP to carbon emission reduction.

[0085]

[0086] In the formula, D carbon (T) represents the alliance's carbon emission reduction, and D... carbon (T\q) represents the carbon emission reduction of the alliance after removing member q.

[0087] The carbon emission reduction contribution factors for each object are as follows:

[0088]

[0089] Assuming that the risk index, satisfaction, and carbon reduction contribution factor of component q have proportions of μ1, μ2, and μ3 respectively in the allocation, we have:

[0090] μ1+μ2+μ3=1

[0091] Taking risk indicator factors as an example, there must be Other:

[0092]

[0093] In the formula, Q represents the number of entities q participating in the distribution within the cooperative alliance.

[0094] ΔE cosy,q ,ΔE carbon,q With ΔE risk,q Similarly, the comprehensive correction factor for participant q can be further obtained as follows:

[0095] ΔC q = (μ1, μ2, μ3) × (ΔE) risk,q ,ΔE cosy,q ,ΔE carbon,q ) T

[0096] This leads to the improved allocation result of the modified element q.

[0097] Compared with the prior art, the present invention has the following beneficial effects:

[0098] (1) High economic benefits and excellent environmental benefits: The multi-virtual power plant optimization operation method disclosed in this invention improves the overall dispatch flexibility and reduces the risk of income. Through direct trading of electricity and carbon quotas between virtual power plants, the overall economic and environmental benefits are effectively improved compared with the operation of virtual power plants alone.

[0099] (2) Fair and reasonable allocation results: The improved weighted Shapley value allocation method disclosed in this invention, which considers risk, satisfaction and carbon emission reduction contribution, effectively reflects the multi-dimensional input and contribution level of each component in the virtual power plant by introducing a comprehensive correction factor, thereby improving the fairness and reasonableness of the allocation and contributing to the stability of long-term cooperative relationships. Attached Figure Description

[0100] Figure 1 This is a flowchart of the method of the present invention;

[0101] Figure 2 For the power generation and consumption prediction of each component inside VPP1-VPP3 in Example 1;

[0102] Figure 3 The market electricity price in Example 1;

[0103] Figure 4 This represents the carbon emission reduction contribution of VPP3 to each party before and after cooperative operation in Example 1. Detailed Implementation

[0104] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0105] Example 1:

[0106] This invention proposes a method for optimized operation and allocation of multiple virtual power plants in the carbon-electricity market, such as... Figure 1 As shown, it includes the following steps:

[0107] Step 1) Consider the ability of virtual power plants to participate in carbon asset management and establish an optimal economic model for multiple virtual power plants that takes into account risk and carbon flow;

[0108] The economic benefits of virtual power plants include revenues from the electricity market and the carbon trading market:

[0109]

[0110] in, For the economic benefits of virtual power plant i at time t, For the revenue that virtual power plants obtain in the electricity market, This refers to the revenue that virtual power plants obtain in the carbon trading market.

[0111] The revenue that the virtual power plant obtains in the electricity market is as follows:

[0112]

[0113] In the formula: These are the benefits of direct power interaction of the virtual power plant i at time t, the grid access fee to be paid, the benefits of trading power with the main grid, the cost of calling up adjustable loads, the cost of calling up energy storage, and the operation and maintenance cost of DER.

[0114] Let t represent the direct transaction price and power between virtual power plant i and virtual power plant j at time t; Let t be the normal transaction price between the virtual power plant and the power grid; Let θ be the interrupt amount of the adjustable load k invoked at time t. k This is the parameter for the adjustable load type; Let be the charging and discharging power of the stored energy e at time t, where η e These correspond to the charging / discharging power and efficiency, respectively. Cost per unit of call; Let m be the power generation output of renewable energy at time t; The penalty electricity price for maintaining power balance when the virtual power plant cannot meet the output requirements at point PCC at time t is denoted as t. It is a 0-1 variable, which is 1 when the power demand is exceeded or insufficient, respectively; The penalties are for exceeding or falling short of demand. γ is the line loss conversion factor, N is the total number of virtual power plants participating in the cooperative alliance, and M is the total number of renewable power generation resources in the virtual power plants. K1 to K6 are positive parameters representing the deviation power that still cannot meet the power requirements of the PCC point after inter-VPP transactions and internal adjustments.

[0115] The revenue that the virtual power plant obtains in the carbon trading market is as follows:

[0116]

[0117] In the formula: These represent the benefits of direct carbon quota interaction with virtual power plant i at time t, and the benefits of trading carbon quotas with the main grid. Let t represent the direct trading carbon allowance price and the direct trading carbon allowance quantity between virtual power plant i and virtual power plant j. The market price of the carbon allowance traded between the virtual power plant and the main grid at time t;

[0118] Let represent the carbon allowance for virtual power plant i at time t. The responsibility for purchased electricity for virtual power plant i at time t.

[0119] As a new type of market participant, virtual power plants can improve their economic efficiency and fully realize their environmental benefits by participating in carbon trading. Carbon emission allowances and certified voluntary emission reductions (CCERs) are currently the two types of traded products in my country's carbon trading market. In this invention, the product traded in the carbon market is the carbon emission allowance. According to the baseline method, the carbon allowance can be described as:

[0120]

[0121] In the formula, The carbon emission factor per unit of electricity. Let m be the amount of electricity generated by renewable energy resource m in virtual power plant i at time t.

[0122] The electricity and carbon trading of virtual power plants are coupled, requiring the optimization of internal resources based on real-time dynamic demand to improve the overall profitability of carbon-electricity trading. As an independent market entity, the virtual power plant's energy supply units are wind and solar power generators. When the renewable energy output of the virtual power plant cannot meet the normal electricity demand, it needs to purchase electricity from the grid. In this case, in addition to incurring the cost of purchasing electricity, the virtual power plant also needs to bear the carbon emissions corresponding to the production of that electricity. The external electricity purchase responsibility of virtual power plant i at time t is described as follows:

[0123]

[0124] In the formula, τ is the proportion of non-clean energy power generation capacity in the regional power grid to the total installed capacity, and κ is the carbon emission responsibility factor corresponding to a unit of non-clean purchased electricity. The purchased electricity of VPP i refers to the deviation electricity that still cannot meet the power balance requirements after direct and internal adjustments between VPPs during the collaborative optimization operation of multiple virtual power plants.

[0125] The optimal operating objective of the multi-virtual power plant economics optimization model considering risk and carbon flow is:

[0126]

[0127] In the formula, For the benefit at time t of virtual power plant i participating in cooperative operation, β i The risk preference coefficient reflects the degree of risk preference of virtual power plant i. For the economic benefits of the virtual power plant i at time t, ρ CVaR,i Let i be the conditional risk value of virtual power plant i.

[0128] The constraints of the multi-virtual power plant economic optimization model considering risk and carbon flow include:

[0129] R1) Energy constraint:

[0130]

[0131]

[0132]

[0133] In the formula, Let i be the total direct interaction power of the virtual power plant at time t; These represent the total predicted DER output and load power consumption of the virtual power plant i at time t, as well as the agreed-upon interactive power at the PCC point.

[0134] R2) Electricity price constraints:

[0135]

[0136]

[0137] In the formula, Let $t$ be the unit grid connection cost when a direct transaction occurs between virtual power plant i and virtual power plant j at time $t$. Let t be the unit operation and maintenance cost of DER within the virtual power plant i at time t. Let be the total cost of calling up adjustable components within virtual power plant i at time t. The price at which the main grid sells carbon allowances to the virtual power plant at time t.

[0138] R3) Adjustable load constraint:

[0139]

[0140]

[0141] In the formula, The maximum number of calls is the adjustable load k. The state of the adjustable load k at time t is a 0 / 1 variable; T IL,kmax This represents the maximum interruption time.

[0142] R4) Energy storage constraints:

[0143]

[0144]

[0145]

[0146]

[0147]

[0148] In the formula, The state of charge of energy storage unit e during time period t; E is a 0 / 1 variable used to determine the discharge and charge states. e,N P ch,e,max P ch,emin P dis,e,max P dis,e,min S soc,e,max S soc,e,min These represent the rated capacity, charge / discharge power, and upper and lower limits of the state of charge of energy storage unit e, respectively.

[0149] R5) Carbon quota trading price and quantity constraints:

[0150] Each virtual power plant is only authorized to sell or purchase carbon allowances at any given time. The trading price and quantity constraints for carbon allowances include:

[0151]

[0152]

[0153]

[0154] In the formula, The price at which the main grid purchases carbon allowances from the virtual power plant at time t. c,s n c,s These represent virtual power plants with power purchase quotas and virtual power plants with power sales quotas, respectively.

[0155] Step 2) Establish a day-ahead optimization cooperative game model for multiple virtual power plants, and solve the cooperative game model to obtain the economic benefits obtained by each virtual power plant in day-ahead operation, as well as the electricity, carbon quota price and quantity traded by each virtual power plant with the upper-level grid and directly traded with other virtual power plants in day-ahead, and the adjustment amount of each adjustable unit.

[0156] The objective function of the current-day optimization cooperative game model for multiple virtual power plants is:

[0157]

[0158] In the formula, N represents the total number of virtual power plants participating in the cooperative alliance. Let be the benefits of virtual power plant i participating in cooperative operation and the optimal benefits of operating alone at time t, respectively. At that time, virtual power plant i will no longer participate in cooperative operation.

[0159] By transforming the cooperative game model using the mean inequality, the solution can obtain the economic benefits obtained by each VPP in day-ahead operation, as well as the electricity, carbon quota price and quantity traded by each VPP with the upstream grid and directly with other VPPs in day-ahead transactions, and the adjustment amount of each adjustable unit.

[0160] Step 3) Introduce a comprehensive correction factor to quantify the impact of allocation factors on the allocation results, construct an improved weighted Shapley value virtual power plant benefit allocation model, and obtain allocation results that reflect the multi-dimensional input and contribution levels of each component in the virtual power plant.

[0161] The improved weighted Shapley value virtual power plant benefit allocation model is as follows:

[0162]

[0163]

[0164] In the formula, This is the result of the allocation of the corrected element q; To correct the change in the economic benefits of component q before and after; The result is the allocation of element q before correction; C i For the overall economic benefits of virtual power plant i; ΔC q The comprehensive adjustment factor is defined as follows: T represents a sub-alliance composed of some participants, x represents economic benefits, and v represents a weighting factor. The allocation influencing factors include risk, satisfaction, and carbon emission reduction contribution; the corresponding adjustment factors include risk indicator factors, satisfaction factors, and carbon emission reduction contribution factors.

[0165] Because the indicators have different dimensions, they need to be preprocessed by normalization before a weighted average is applied to obtain the final allocation scheme. The calculation process for each correction factor is as follows:

[0166] 1) Risk indicator factors

[0167] Wind power, photovoltaic power, and conventional loads are defined as non-adjustable components, while adjustable loads and energy storage units are defined as adjustable components. These two types of components exhibit risk aversion and risk preference, respectively, in relation to prediction deviations. Based on utility theory, commonly used utility functions are selected and marginal values ​​are substituted to quantify the risk utility of each type of component.

[0168]

[0169]

[0170] In the formula, U(f) q1 f is a utility function with risk-averse components. q1 ={f PV ,fWT ,f LOAD} represents the risk adaptation function for non-adjustable components, which is related to prediction bias; This is the predicted value for non-adjustable components.

[0171]

[0172]

[0173] In the formula, U(f) q2 f is a utility function for components with risk preferences. q2 ={f IL ,f ess} is the risk adaptation function of the schedulable object, and its value is related to the maximum adjustable amount of the schedulable object. And it is related to the prediction accuracy of unschedulable objects.

[0174] The risk factors for each object are:

[0175]

[0176] 2) Satisfaction Factor

[0177] Among the various components within the VPP, only adjustable loads are considered to require a satisfaction factor; the satisfaction factors for other components are set to 0. The root mean square error (RMSE) is used to measure the difference in adjustable load curves before and after optimization, describing the degree of sacrifice in electricity user satisfaction. The satisfaction factors for each component are as follows:

[0178]

[0179]

[0180] In the formula, K is the set of adjustable loads. These represent the interruption amount of the adjustable load k at time t and the power consumption of the adjustable load before optimization, respectively.

[0181] 3) Carbon emission reduction contribution factors

[0182] Wind power, solar power, and various demand response resources are the main drivers of carbon emission reduction. Conversely, electricity-consuming load units are the main indirect contributors to carbon emissions. Based on this, a carbon emission reduction contribution model was established to analyze the contribution of each component within a VPP to carbon emission reduction.

[0183]

[0184] In the formula, D carbon (T) represents the alliance's carbon emission reduction, and D... carbon (T\q) represents the carbon emission reduction of the alliance after removing member q.

[0185] The carbon emission reduction contribution factors for each object are as follows:

[0186]

[0187] Assuming that the risk index, satisfaction, and carbon reduction contribution factor of component q have proportions of μ1, μ2, and μ3 respectively in the allocation, we have:

[0188] μ1+μ2+μ3=1

[0189] Taking risk indicator factors as an example, there must be Other:

[0190]

[0191] In the formula, Q represents the number of entities q participating in the distribution within the cooperative alliance.

[0192] ΔE cosy,q ,ΔE carbon,q With ΔE risk,q Similarly, the comprehensive correction factor for participant q can be further obtained as follows:

[0193] ΔC q = (μ1, μ2, μ3) × (ΔE) risk,q ,ΔE cosy,q ,ΔE carbon,q ) T

[0194] This leads to the improved allocation result of the modified element q.

[0195] The virtual power plant-grid system studied in this embodiment includes three virtual power plants: VPP1 and VPP2 contain wind power, solar power, conventional loads, adjustable loads, and energy storage; VPP3 contains conventional loads, adjustable loads, and energy storage. The power generation and consumption forecast information for wind power, solar power, and loads within each virtual power plant is as follows: Figure 2 As shown, the market electricity price is as follows Figure 3 As shown, the prices for purchasing and selling carbon quotas on the main online platform are 30 yuan / ton and 50 yuan / ton, respectively.

[0196] To compare the benefits of different operating schemes under the background of electricity trading and carbon quota trading for virtual power plants, this embodiment sets up two schemes as follows:

[0197] Option A1: Each VPP operates independently and only transacts with the mainnet;

[0198] Option A2: Each VPP operates collaboratively, enabling direct transactions with other VPPs and transactions with the mainnet.

[0199] Table 1 shows that the overall benefit of VPP cooperative operation is 4136 yuan better than operating alone. Specifically, the benefits of VPP1, VPP2, and VPP3 increased by 13.63%, 12.91%, and 8.72%, respectively. Since both direct electricity trading prices and direct carbon quota trading prices are lower than the grid sales price but higher than the grid buyback price, VPPs prefer to prioritize direct electricity and carbon quota trading with other VPPs within the cooperative alliance, rather than trading electricity and carbon quotas directly with the grid, thereby reducing costs.

[0200] Table 1. Operational benefits of each VPP under different schemes

[0201] Option A1 / yuan 10467 12520 -12410 10577 Option A2 / yuan 11894 14147 -11328 14713

[0202] Figure 4 This demonstrates the carbon emission reduction contributions of each party before and after the cooperative operation of VPP3. Besides the impact of demand response to dispatchable resources within VPP3, the reasons for this result are as follows: First, cooperative operation reduces the amount of electricity purchased by VPPs that do not meet their power demands, directly leading to a decrease in the overall carbon allowance purchase demand of VPPs, thus achieving carbon emission reduction; Second, if VPPs have a need to purchase carbon allowances, this can be prioritized through direct carbon allowance trading between VPPs, eliminating the need to purchase non-clean energy generation from the upper-level grid, thereby achieving carbon emission reduction.

[0203] Based on the day-ahead transaction results of each VPP, the benefits of each component within VPP1 are allocated. The economic benefits of the components within VPP1 under different allocation schemes are shown in Table 2.

[0204] Option B1: Traditional Shapley value allocation method;

[0205] Option B2: Improved weighted Shapley value allocation method.

[0206] Table 2. Benefits of VPP1 internal components under different schemes

[0207] Option B1 / yuan 12113 16605 -17587 310 453 Option B2 / yuan 11915 16708 -19615 1230 1655

[0208] As shown in Table 2, Scheme B2 considers risk indicator factors, and the allocation results for photovoltaic, wind power, and conventional loads are lower compared to Scheme B1. However, it also considers carbon emission reduction factors, and the wind power units show greater benefits due to their more prominent carbon emission reduction performance. The inclusion of the satisfaction factor allows adjustable load units to receive additional compensation. Adjustable loads and energy storage can benefit from risk management and play a positive role in promoting carbon emission reduction; therefore, using Scheme B2 for allocation can result in a significant increase in benefits.

[0209] In summary, the cooperative operation of VPPs outperforms individual VPP operation in both economic and environmental benefits; the allocation results of the improved weighted Shapley value method reflect the multi-dimensional contribution levels of each component. Therefore, it can be concluded that the proposed method for optimizing the operation and allocation of multiple virtual power plants can achieve optimal operation and allocation of virtual power plants.

[0210] The proposed multi-virtual power plant (VPP) day-ahead optimization cooperative game model fully considers the various risks faced by VPPs, explores their economic dispatch capabilities and carbon asset management capabilities, effectively achieves resource complementarity, improves overall dispatch flexibility, reduces revenue loss risk, and overcomes the problems of low dispatch flexibility and high risk loss faced by independent entities. Therefore, this method has the advantages of high economic and environmental benefits. Furthermore, this method considers risk, satisfaction, and carbon emission reduction contribution factors, constructing an improved weighted Shapley value VPP benefit allocation model based on a comprehensive correction factor, which can effectively reflect the multi-dimensional input and contribution levels of each component in the VPP. Therefore, this method has the advantages of improving the fairness and rationality of allocation and contributing to the stability of long-term cooperative relationships.

[0211] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for optimized operation and allocation of multiple virtual power plants in a carbon-electricity market, characterized in that, Includes the following steps: Considering the ability of virtual power plants to participate in carbon asset management, establish an optimal economic benefit model for multiple virtual power plants that takes into account risk and carbon flow. A multi-virtual power plant day-ahead optimization cooperative game model is established. The cooperative game model is solved to obtain the economic benefits obtained by each virtual power plant in day-ahead operation, as well as the electricity, carbon quota price and quantity traded by each virtual power plant with the upper-level grid and directly traded with other virtual power plants in day-ahead, and the adjustment amount of each adjustable unit. By introducing a comprehensive correction factor to quantify the impact of various factors on the allocation results, an improved weighted Shapley value virtual power plant benefit allocation model is constructed. This model yields allocation results that reflect the multi-dimensional input and contribution levels of each component in the virtual power plant. The objective function of the current-day optimization cooperative game model for multiple virtual power plants is: In the formula, N The total number of virtual power plants participating in the cooperative alliance. , They are respectively t Virtual Power Plant i The benefits of participating in cooperative runtime and the optimal benefits of running alone, when At that time, virtual power plant i No longer participating in cooperative operations, The improved weighted Shapley value virtual power plant benefit allocation model is as follows: In the formula, For the corrected components q The distribution of income; To correct the components before and after q The change in economic benefits; For the component before correction q The distribution of income; For virtual power plants i The overall economic benefits; As a comprehensive correction factor, T It is a sub-alliance composed of some participants. x For economic benefits, v Weighting factor, The allocation factors include risk, satisfaction, and carbon emission reduction contribution; the corresponding correction factors include risk indicator factors, satisfaction factors, and carbon emission reduction contribution factors. The comprehensive correction factor is calculated by weighted averaging of each correction factor after normalization. in, , , For weight parameters, These are the normalized risk indicator factor, satisfaction factor, and carbon emission reduction contribution factor, respectively.

2. The method for optimized operation and allocation of multiple virtual power plants in a carbon-electricity market according to claim 1, characterized in that, The economic benefits of virtual power plants include revenues from the electricity market and the carbon trading market: in, For virtual power plants i exist t The economic benefits of time For the revenue that virtual power plants obtain in the electricity market, This refers to the revenue that virtual power plants obtain in the carbon trading market.

3. The method for optimized operation and allocation of multiple virtual power plants in a carbon-electricity market according to claim 2, characterized in that, The revenue that the virtual power plant obtains in the electricity market is as follows: In the formula: , , , , , They are respectively t Virtual Power Plant i Benefits of direct power exchange, grid access fees, benefits of trading power with the main grid, cost of calling adjustable loads, cost of calling energy storage, and operation and maintenance costs of DER.

4. The method for optimized operation and allocation of multiple virtual power plants in a carbon-electricity market according to claim 2, characterized in that, The revenue that the virtual power plant obtains in the carbon trading market is as follows: In the formula: , They are respectively t Virtual Power Plant i Benefits of direct carbon quota interaction and benefits of trading carbon quotas with the main grid.

5. The method for optimized operation and allocation of multiple virtual power plants in a carbon-electricity market according to claim 1, characterized in that, The optimal operation objective of the multi-virtual power plant economics optimization model considering risk and carbon flow is: In the formula, For virtual power plants i Participating in the collaborative runtime t The benefits of time This is the risk preference coefficient, reflecting the virtual power plant i The degree of risk preference; For virtual power plants i exist t The economic benefits of time For virtual power plants i Conditional risk value.

6. The method for optimized operation and allocation of multiple virtual power plants in a carbon-electricity market according to claim 1, characterized in that, The constraints of the multi-virtual power plant economic efficiency optimization model that considers risk and carbon flow include electricity constraints, electricity price constraints, adjustable load constraints, energy storage constraints, and carbon quota trading price and quantity constraints.

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