Green electricity-carbon market linkage-based green electricity transaction matching method

Abstract

The invention relates to the technical field of electricity transaction, in particular to a green electricity-carbon market linkage-based green electricity transaction matching method, which comprises the following steps of S1, data acquisition and preprocessing: acquiring green electricity transaction data, and cleaning, screening and preprocessing the acquired green electricity transaction data; s2, establishing a green electricity-carbon market linkage model: establishing the green electricity-carbon market linkage model; s3, green electricity transaction matching calculation: calculating an optimal green electricity transaction matching scheme to realize cost and benefit optimization; s4, risk assessment and control: assessing the risk of the transaction scheme, and formulating risk control measures; s5, transaction execution and monitoring: executing the transaction according to the transaction scheme and performing real-time monitoring, collecting and analyzing data, and dynamically adjusting and optimizing the scheme; according to the invention, energy structure optimization and greenhouse gas emission reduction targets are promoted to be realized, and powerful support is provided for development of renewable energy sources and carbon emission reduction.

Description

Technical Field

[0001] The present invention relates to the technical field of power trading, and in particular to a green electricity trading matching method based on green electricity-carbon market linkage. Background Art

[0002] Under the severe situation of global climate change, reducing greenhouse gas emissions and promoting energy transformation have become urgent tasks. Green electricity, that is, electricity converted from renewable energy such as solar energy, wind energy, hydropower, etc., has become a key development direction in the energy field due to its clean, low-carbon and sustainable characteristics. At the same time, the carbon market, as a greenhouse gas emission reduction tool based on market mechanisms, is also gradually emerging around the world.

[0003] With the strong support of various countries for the development of renewable energy and the introduction of relevant regulations, the scale of green electricity has continued to expand, and its proportion in energy supply has gradually increased. However, the development of green electricity still faces a series of challenges. On the one hand, the production cost of green electricity is relatively high, and its price is often at a disadvantage in market competition. On the other hand, the supply of green electricity is intermittent and uncertain, which has brought certain pressure to the stable operation of the power system. The establishment of the carbon market aims to set a price for carbon emissions and encourage companies to reduce carbon emissions. Companies can meet their emission needs by purchasing carbon emission quotas, or obtain excess quotas by taking emission reduction measures and sell them on the market. This mechanism has promoted energy conservation and emission reduction by companies to a certain extent, but there are also some problems in actual operation.

[0004] At present, the linkage mechanism between green electricity trading and carbon market is not yet mature. In most cases, green electricity trading and carbon market operate independently of each other, lacking effective coordination and integration, which has led to a series of adverse consequences. First, the environmental value of green electricity has not been fully reflected in the market. Since the advantages of green electricity in reducing carbon emissions are not directly linked to the carbon market, green electricity suppliers cannot obtain corresponding economic returns, which affects their enthusiasm for investment and development. Secondly, when choosing electricity supply, electricity users often only consider price factors and ignore the environmental benefits of green electricity, which makes it difficult for green electricity to increase its market share quickly, which is not conducive to the optimization of energy structure and the realization of greenhouse gas emission reduction targets. In addition, due to the information asymmetry between green electricity trading and carbon market, it is difficult for market participants to accurately assess the value of green electricity and the cost-effectiveness of carbon emission reduction, resulting in wrong decision-making and waste of resources. For example, some companies may rely too much on purchasing carbon emission quotas and ignore the use of green electricity to achieve emission reduction; and some green electricity suppliers may be unable to effectively promote their products due to lack of understanding of the carbon market.

[0005] To sum up, the current separation between green electricity trading and carbon market has seriously restricted the development of renewable energy and the progress of greenhouse gas emission reduction. Therefore, it is of great practical significance and urgency to establish a green electricity trading matching method based on the linkage between green electricity and carbon market, which can promote the consumption of green electricity, improve energy utilization efficiency and promote the sustainable development of economy and society. Summary of the invention

[0006] The present invention provides a green electricity transaction matching method based on green electricity-carbon market linkage.

[0007] A green electricity transaction matching method based on green electricity-carbon market linkage includes the following steps:

[0008] S1, data collection and preprocessing: collect green electricity transaction data, including green electricity supply price, user electricity demand, carbon emission quota and carbon trading price, and clean, screen and preprocess the collected green electricity transaction data. The preprocessing includes data standardization, data smoothing and dimensionality reduction processing;

[0009] S2, establish a green electricity-carbon market linkage model: comprehensively consider supply costs, electricity demand, electricity purchase costs, carbon emission quota allocation and trading, and carbon trading prices to establish a green electricity-carbon market linkage model;

[0010] S3, Green power transaction matching calculation: Based on user demand and supply capacity, combined with price factors, calculate the optimal green power transaction matching plan to achieve optimal cost and benefit;

[0011] S4, Risk Assessment and Control: Assess the risks of the transaction plan, including market, regulatory, and technical risks, and formulate risk control measures;

[0012] S5, Transaction Execution and Monitoring: Execute transactions according to the transaction plan and monitor in real time, collect and analyze data, and dynamically adjust the optimization plan.

[0013] Optionally, the data collection and preprocessing in S1 includes:

[0014] S11, data collection: collecting green electricity supply prices, user electricity demand, carbon emission quotas and carbon trading prices;

[0015] S12, data cleaning and screening: check the abnormal values ​​of green electricity transaction data, and remove or correct any abnormal data found;

[0016] S13, data preprocessing: data standardization, data smoothing and dimension reduction processing are performed on the collected green electricity transaction data;

[0017] The data smoothing adopts moving average method for smoothing;

[0018] The dimension reduction process performs data dimension reduction by using the principal component analysis (PCA) method.

[0019] Optionally, the establishment of a green electricity-carbon market linkage model in S2 includes:

[0020] S21, model building: calculate the green electricity supply cost, and build a green electricity-carbon market linkage model through the linkage mechanism between green electricity price and carbon trading price;

[0021] S22, consider uncertainty factors: build a risk adjustment model by analyzing the standard deviation of price fluctuations and risk sensitivity coefficient;

[0022] S23, price correction: introduce seasonal factors, regulatory factors and market competition factors to correct the green electricity price and carbon trading price.

[0023] Optionally, the model building in S21 includes:

[0024] S211, cost calculation: Calculate the green electricity supply cost, expressed as:

[0025]

[0026] Among them, C g0 is the fixed cost, k1, k2, k3, k4 are cost coefficients, Q g is the green electricity supply, E g Energy conversion efficiency for green electricity supply, T g is the time factor of green electricity supply, R g Reliability indicators for green electricity supply;

[0027] S212, Model establishment: Based on the green electricity supply cost C g , combined with the electricity demand of power users D u , Power purchase cost C u 、Allocation of Carbon Emission Quota A c and trading and carbon trading price P c A green electricity-carbon market linkage model is established, which can be expressed as:

[0028] F(C g , D u , C u , A c , P c )=0;

[0029] S213, Establishing a linkage mechanism: Constructing a green electricity price P g and carbon trading price P c The association mechanism is expressed as:

[0030]

[0031] Among them, k5, b1, k6, and k7 are correlation coefficients.

[0032] Optionally, the risk adjustment model in S22 is expressed as:

[0033] P′ g =P g (1+α1σ g );

[0034] P′ c =P c (1+α2σ c );

[0035] Among them, α1 and α2 are risk sensitivity coefficients, σ g and σ c are the standard deviations of price fluctuations of green electricity and carbon trading, P′ g and P′ c They are the adjusted green electricity price and carbon trading price respectively.

[0036] Optionally, the price correction in S23 is expressed as:

[0037] P g ″=P′ g (1+β1S season )(1+β2P policy )(1+β3M competiton );

[0038] P″ c =P′ c (1+γ1S season )(1+γ2P policy )(1+γ3M competiton );

[0039] Among them, P g and P″ c are the revised green electricity price and carbon trading price respectively, β1, β2, β3, γ1, γ2, γ3 are the corresponding impact coefficients, S season , P policy 、M competiton They are seasonal factors, regulatory factors, and market competition factors.

[0040] Optionally, the green electricity transaction matching calculation in S3 includes:

[0041] S31, define the objective function: according to the power demand D of the power user u and the supply capacity of green electricity suppliers Q g , combined with the green electricity price P g and carbon trading price P c, the optimal green electricity trading matching scheme is calculated through the optimization algorithm, so that the objective function can obtain the minimum value under the constraints. The objective function is expressed as:

[0042] Z=min(C g +C u +λ1R g +λ2R c +λ3E env );

[0043] Among them, λ1 and λ2 are risk weight coefficients, λ3 is the environmental weight coefficient, and E env for environmental benefits;

[0044] S32, Constraints: Define constraints, expressed as:

[0045] Q g ≥D u ;

[0046] C u ≤C u，max ;

[0047] A c ≥A c，min ;

[0048] P g ∈(P g，min , P g，max );

[0049] P c ∈(P c，min , P c，max );

[0050] E env ≥E env，min ;

[0051] S soc ≥S soc，mm ;

[0052] p policy (Q g , A c , P c )≤0;

[0053]

[0054] Among them, P policy is the specified constraint function, k s , b2 is the market equilibrium coefficient.

[0055] Optionally, the risk assessment and control in S4 includes:

[0056] S41, Market risk: Market risk includes green electricity price fluctuation risk, carbon trading price fluctuation risk and green electricity supply interruption risk, expressed as:

[0057] ΔP g =P g (t1)-P g (t0);

[0058] ΔP c =P c (t1)-P c (t0);

[0059]

[0060]

[0061] Among them, ΔP g and ΔP c are the green electricity fluctuation price and carbon trading fluctuation price, P g (t1) and P c (t1) are the current green electricity price and the current carbon trading price, respectively. g (t0) and P c (t0) are the green electricity price and carbon trading price in the previous time period, ω g and ω c They are green electricity price volatility and carbon trading price volatility respectively;

[0062] S42, Regulatory Risk: Evaluate and calculate the impact of changes in carbon emission regulations based on regulatory parameters, enforcement strength and timeliness, expressed as:

[0063] E p =f2(P p , L p , T p );

[0064] Among them, P p is the specified parameter, L p For the enforcement strength of the regulations, T p For the timeliness of the provisions;

[0065] S43, Technical Risk: By analyzing the green electricity supply, energy conversion efficiency, the covariance of supply and conversion efficiency, and reliability indicators, the stability risk of green electricity supply is evaluated, expressed as:

[0066] S g =β1V ar (Q s )+β2V ar (E g )+β3C ov (Qg , E g )+β4V ar (R g );

[0067] Among them, β1, β2, β3, β4 are stability weight coefficients, V ar represents the variance, C ov represents covariance;

[0068] S44, Risk Control Strategy: Formulate corresponding risk control strategies, including price fluctuation warning, establishment of risk reserve and risk hedging strategies;

[0069] The price fluctuation warning sets a price fluctuation warning threshold. When the green electricity price or carbon trading price exceeds the predetermined threshold, corresponding measures are taken, including adjusting the trading strategy or performing risk hedging, which is expressed as:

[0070] |ΔP g |>θ g or |ΔP c |>θ c ;

[0071] Among them, θ g and θ c is the warning threshold;

[0072] The risk reserve fund is established by establishing a risk reserve fund model to provide financial support when risk events occur, which is expressed as:

[0073] R reserve =k 10 (|ΔP g |+|ΔP c |+|E p |+|S g |+|I g |);

[0074] Among them, k 10 is the reserve factor;

[0075] The risk hedging strategy hedges price risk through futures contracts or options contracts, which can be expressed as:

[0076] F futures =k 11 (P g，expected -P g，actual );

[0077] O options =k 12 max(0,P g，expected -P g，actual );

[0078] Among them, Ffutures is the futures contract value, O options is the option contract value, k 11 , k 12 is the contract coefficient.

[0079] Optionally, the transaction execution and monitoring in S5 includes:

[0080] S51, real-time monitoring: real-time monitoring of the transaction process, collecting the actual supply of green electricity Q g，actual , power consumption D u，actual and the transaction price execution g，executed ;

[0081] S52, Deviation Analysis: Deviation analysis is performed on the difference between the actual monitoring data and the expected data to identify the source and size of the deviation in the transaction process, expressed as:

[0082]

[0083]

[0084]

[0085] S53, Transaction efficiency index calculation: By setting the transaction efficiency index, the efficiency and effect of the entire transaction process are measured and the quality of transaction execution is evaluated, which is expressed as:

[0086]

[0087] S54, plan adjustment: adjust the plan based on the monitoring results, including optimizing the trading plan, applying adaptive control algorithms, and using machine learning algorithms to assist decision making.

[0088] Optionally, the scheme adjustment in S54 includes:

[0089] S541, optimize the transaction plan: according to the results of real-time monitoring and deviation analysis, when the actual supply of green electricity, electricity consumption, transaction price execution and transaction efficiency exceed the preset threshold, the plan is optimized, which is expressed as:

[0090] |Q g，actual -Q g |>∈ q ;

[0091] |D u，actual -D u |>∈ d ;

[0092] |P g，executed -P g |>∈ p ;

[0093] E trans <∈ e ;

[0094] S542, Adaptive control algorithm application: Use the adaptive control algorithm to dynamically adjust the trading plan to adapt to real-time changes, expressed as:

[0095] Adjustment=

[0096] f3(Deviation g , Deviation u , Deviation p , E trans , HistoricalDate);

[0097] S543, machine learning algorithm assisted decision-making: using neural networks to analyze and predict monitoring data, and discover potential problems and risks in advance, expressed as:

[0098] O = f4(WX+b);

[0099] Among them, W is the weight matrix, X is the input data, b is the bias term, and f4 is the activation function.

[0100] Beneficial effects of the present invention:

[0101] The present invention, by integrating green electricity trading with the carbon market, enables the environmental value of green electricity to be fully reflected in the market, which not only improves the economic returns of green electricity suppliers and stimulates their enthusiasm for investment and development, but also encourages electricity users to consider the environmental benefits of green electricity more when purchasing electricity, thereby promoting the optimization of energy structure and the realization of greenhouse gas emission reduction goals, and providing strong support for the development of renewable energy and carbon emission reduction.

[0102] The present invention establishes a linkage model by comprehensively considering supply costs, user needs, carbon emission quotas and carbon trading prices, which can accurately reflect the market supply and demand relationship and calculate the optimal green electricity trading matching plan through an optimization algorithm. This mechanism can effectively reduce market decision-making errors and resource waste caused by information asymmetry, improve market efficiency and the rationality of resource allocation, and at the same time, through risk assessment and control, reduce market risks and uncertainties brought by regulations and technologies, and ensure the stability and reliability of transactions.

[0103] The present invention, through real-time monitoring, deviation analysis and scheme adjustment and other measures, enables the transaction process to have strong dynamic adaptability. The introduction of adaptive control algorithm and machine learning algorithm enables the transaction scheme to be dynamically adjusted according to real-time data, so as to respond to market changes and potential risks in a timely manner. This not only improves the execution efficiency of the transaction, but also makes the entire green electricity trading system more flexible and intelligent, which can better meet market demand and promote the sustainable development of the economy and society. BRIEF DESCRIPTION OF THE DRAWINGS

[0104] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0105] Figure 1 A schematic diagram of the data collection and preprocessing process of an embodiment of the present invention;

[0106] Figure 2 This is a schematic diagram of a green electricity-carbon market linkage model according to an embodiment of the present invention;

[0107] Figure 3 The figure is a flow chart of a configuration method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0108] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0109] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0110] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0111] Assume that there is a green electricity supplier A and an electricity user B in a certain area. In a certain period of time, green electricity transactions are matched.

[0112] like Figure 1-Figure 3 As shown, a green electricity transaction matching method based on green electricity-carbon market linkage includes the following steps:

[0113] 1. Data collection and preprocessing:

[0114] 1. Data collection:

[0115] Data provided by green electricity supplier A: Green electricity supply Q g For 100 MWh, the energy conversion efficiency E g is 0.4, the supply time factor T g is daytime (assuming the influence coefficient during the day is 1), the reliability index R g is 0.95. Green electricity supply price P g According to the function P g =f1(Q g , E g , T g , R g )=0.5+0.01Q g +0.2 / T g +0.05R g Calculate and get P g It is 0.65 yuan / kWh.

[0116] Data provided by electricity user B: initial electricity demand D u The time variation coefficient of electricity demand is 80 MWh, k t is 0.5, and the economic development coefficient k e is 0.3, the current time t is 10 hours, and the economic development index E eco 2. Electricity demand D u (t,E eco )=D u0 +k t t+k e E eco =80+0.5×10+0.3×2=86.6

[0117] MWh, electricity purchase cost budget C u 500,000 yuan.

[0118] Data obtained from the carbon trading platform: Carbon emission quota A c is 80 tons, and the carbon trading price is P c The dynamic relationship between carbon emission quota and carbon trading price is:

[0119] A c =g1(P c , E c , S c , P oil , P gas )=100-0.5P c +0.1E c +0.05S c +0.02P oil +0.01P gas ;

[0120] Among them, E c , S c , P oil , P gas The specific value is set according to the actual situation.

[0121] 2. Data cleaning and screening:

[0122] Check whether there are abnormal values ​​in the data, such as whether the green electricity supply is within a reasonable range, whether the carbon trading price is in line with market conditions, etc. If abnormal data is found, remove or correct it.

[0123] 3. Data preprocessing:

[0124] Data standardization: Assume that the mean of the original data is X mean is 50, standard deviation X std is 10, then the green electricity supply price P g The standardized value of is:

[0125]

[0126] Data smoothing: Using the moving average method, assuming that the first three data are taken for smoothing, it can be expressed as:

[0127]

[0128] Dimensionality reduction: By using the principal component analysis (PCA) method, assuming that two main features are extracted, the data after dimensionality reduction is expressed as:

[0129] Z=U T X;

[0130] Among them, U is the eigenvector matrix and X is the original data matrix.

[0131] 2. Establish a green electricity-carbon market linkage model:

[0132] 1. Build the model:

[0133] Green electricity supply cost:

[0134] Assume that the fixed cost C g0 The cost is 100,000 yuan, the cost coefficient k1 is 0.05, k2 is 0.2, k3 is 0.1, k4 is 0.05, then Cg = 10 + 0.05 × 100 + 0.2 / 0.4 + 0.1 × 1 2 +0.05×0.95 1 . 5 =155,1875 yuan.

[0135] Model building: F(C g , D u , C u , A c , P c )=0;

[0136] The specific form will be determined based on actual conditions.

[0137] The linkage mechanism between green electricity price and carbon trading price is:

[0138]

[0139] Assuming the correlation coefficient k5 is 0.2, b1 is 0.5, k6 is 0.1, and k7 is 0.05, then P g =0.2×50+0.5+0.1×ln(50)+0.05×50 2 =13.83256 yuan / kWh.

[0140] 2. Consider uncertainty factors:

[0141] The risk sensitivity coefficient α1 is 0.1, α2 is 0.2, and the standard deviation of price fluctuations of green electricity and carbon trading is σ g is 0.05, σ c If is 0.1, the risk-adjusted green electricity price is:

[0142] P′ g =P g (1+α1σ g )=50×(1+0.1×0,05)=13.901728 yuan / kWh;

[0143] The risk-adjusted carbon trading price is:

[0144] P′ c =P c (1+α2σ c ) = 50 × (1 + 0.2 × 0.1) = 51 yuan / ton;

[0145] Among them, P′ g and P′ c They are the adjusted green electricity price and carbon trading price respectively.

[0146] 3. Introduce other factors to modify the model:

[0147] Seasonal factor index S season For summer (assuming the influence coefficient of summer is 1.1), the factor index P is specified. policy is 0.9 (indicating that the regulations are conducive to green electricity trading), and the market competition factor index M competiton The impact coefficient β1 is 0.05, β2 is 0.1, β3 is 0.08, γ1 is 0.06, γ2 is 0.09, and γ3 is 0.07. The revised green electricity price is:

[0148] P″ g =P′ g (1+β1S season )(1+β2P policy )(1+β3M competiton )=13.901728×1.1×0.9×0.8=10.86738304 yuan / kWh;

[0149] The revised carbon trading price is:

[0150] P″ c =P′ c (1+γ1S season )(1+γ2P policy )(1+γ3M competiton ) = 51 × 1.1 × 0.9 × 0.8 = 40.452 yuan / ton;

[0151] Among them, P″ g and P″ c They are the revised green electricity price and carbon trading price respectively.

[0152] 3. Green power transaction matching calculation:

[0153] 1. Objective function setting:

[0154] The objective function is: Z = min(C g +C u +λ1R g +λ2R c +λ3Eenv );

[0155] Assume that the risk weight coefficient λ1 is 0.1, λ2 is 0.2, the environmental weight coefficient λ3 is 0.3, and the environmental benefit E env is 20 (derived from the environmental benefit assessment of green electricity use), then the objective function is:

[0156] Z=min(15.51875+50+0.1R g +0.2R c +0.3×20);

[0157] 2. Constraint setting:

[0158] Q g ≥D u , that is, 100>86.6, which meets the conditions.

[0159] C u ≤C u，max , assuming C u，max It is 600,000 yuan, 50≤60, which meets the conditions.

[0160] A c ≥A c，min , assuming A c，min It is 70 tons, 80≥70, which meets the conditions.

[0161] P g ∈(P g，min , P g，max ), assuming P g，min 10 yuan / kWh, P g，max It is 20 yuan / kWh, 10.86738304∈(10,20), which meets the conditions.

[0162] P c ∈(P c，min , P c，max ), assuming P c，min 40 yuan / ton, P c，max It is 60 yuan / ton, 40.452∈(40,60), which meets the conditions.

[0163] E env ≥E env，min , assuming E env，min is 15, 20≥15, which meets the condition.

[0164] S soc ≥S soc，mmin , assuming S soc，min is 0.8, calculated based on the social sustainable development index. soc is 0.9, which meets the conditions.

[0165] Specify the constraint condition P policy (Q g , A c , P c )≤0, the specific constraint function is set according to the actual regulations.

[0166] Market equilibrium conditions Assume that the market equilibrium coefficient k s is 0.05, b2 is 0.1, then 100×86.6 -1 =0.05×10.86738304×40.425 -1 +0.1≈1.158967 satisfies the condition.

[0167] 3. Optimization algorithm solution:

[0168] The linear programming algorithm is used to solve the optimal green electricity transaction matching plan, and the amount of green electricity supplied by green electricity supplier A to electricity user B and the transaction price are obtained.

[0169] 4. Risk assessment and control:

[0170] 1. Risk Assessment:

[0171] Market risk: Assume that the green electricity price P in the previous period g (t0) is 12 yuan / kWh, and the current green electricity price P g (t1) is 10.86738304 yuan / kWh, then the green electricity price fluctuation risk is:

[0172] ΔP=P g (t1)-P g (t0) = 10.86738304-12 = -1.13261696 yuan / kWh, the price volatility is:

[0173]

[0174] The calculation of carbon trading price volatility risk is similar to that of price volatility.

[0175] Green power supply interruption risk: Assuming the interruption loss coefficient k g is 100,000 yuan, the probability of green power supply interruption Pr(Q g =0) is 0.05, then the risk of green power supply interruption is:

[0176] I g =k g Pr(Q g =0)=10×0.05=0.5 million yuan.

[0177] Regulatory risk: The impact of changes in carbon emission regulations, expressed as:

[0178] E p =f2(P p , L p , T p );

[0179] The specific function form is set according to the specified parameters, execution strength and timeliness.

[0180] Technical risk: Assuming the stability weight coefficient β1 is 0.2, β2 is 0.3, β3 is 0.2, β4 is 0.3, and the variance of green power supply V ar (Q s ) is 0.1, the variance of energy conversion efficiency V ar (E g ) is 0.05, the covariance C between green electricity supply and energy conversion efficiency ov (Q g , E g ) is 0.02, and the variance of the reliability index V ar (R g ) is 0.01, then the green electricity supply stability risk is:

[0181] S g =β1V ar (Q s )+β2V ar (E g )+β3C ov (Q g , E g )+β4V ar (R g )=0.2×0.1+0.3×0.05+0.2×0.02+0.3×0.01=0.042.

[0182] 2. Risk control strategy:

[0183] Set the price fluctuation warning threshold, assuming that the green electricity price warning threshold θ g is 1 yuan / kWh, and the carbon trading price warning threshold θ c is 5 yuan / ton, when |ΔP g |>θ g or |ΔP c |>θ c Take corresponding measures when the market is at risk, such as adjusting trading strategies or hedging risks.

[0184] Build a risk reserve model:

[0185] R reserve =k 10 (|ΔP g |+|ΔP c |+|Ep |+|S g |+|I g |);

[0186] Assuming the reserve factor k 10 is 0.2, then R reserve =0.2(|-1.13261696|+|ΔP c |+|E p |+0.041+0.5);

[0187] Adopt risk hedging strategies, such as hedging price risks through futures contracts or options contracts. Assume that the value of the futures contract is:

[0188] F futures =k 11 (P g，expected -P g，actual );

[0189] The option contract value is:

[0190] O options =k 12 max(0,P g，expected -P g，actual );

[0191] Contract coefficient k 11 is 0.8, k 12 is 0.5.

[0192] 5. Transaction Execution and Monitoring:

[0193] 1. Transaction Execution:

[0194] According to the determined green electricity transaction matching plan, green electricity supplier A supplies green electricity to electricity user B.

[0195] 2. Real-time monitoring:

[0196] Real-time monitoring of the actual supply of green electricity Q g，actual , power consumption D u，actual , Transaction Price Execution g，executed Assuming that the actual monitored green electricity supply is Q g，actual 90 MWh, power consumption D u，actual The transaction price is 85 MWh. g，executed It is 10.5 yuan / kWh.

[0197] 3. Deviation analysis:

[0198] The deviation analysis formula is:

[0199]

[0200]

[0201]

[0202] 4. Transaction efficiency index calculation:

[0203] The transaction efficiency indicators are:

[0204]

[0205] 5. Plan adjustment:

[0206] According to the monitoring results, if |Q g，actual -Q g |>∈ q (assume 5%) or |D u，actual -D u |>∈ d (assume 2%) or |P g，executed -P g |>∈ p (assumed to be 3%) or E trans <∈ e (Assuming it is 85%), the solution is optimized.

[0207] Adaptive control algorithm is used for dynamic adjustment, which is expressed as:

[0208] Adjustment = f3(Deviation g ,Deviation u ,Deviation p ,E trans , HistoricalDate).

[0209] Use machine learning algorithms, such as neural networks or decision trees, to analyze and predict monitoring data and identify potential problems and risks in advance. For example, the output of a neural network is:

[0210] O = f4(WX+b);

[0211] Among them, W is the weight matrix, X is the input data, b is the bias term, and f4 is the activation function.

[0212] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0213] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A green electricity transaction matching method based on green electricity-carbon market linkage, characterized in that: The following steps are involved: S1, data collection and preprocessing: collect green electricity transaction data, including green electricity supply price, user electricity demand, carbon emission quota and carbon trading price, and clean, screen and preprocess the collected green electricity transaction data. The preprocessing includes data standardization, data smoothing and dimensionality reduction processing; S2, establish a green electricity-carbon market linkage model: comprehensively consider supply costs, electricity demand, electricity purchase costs, carbon emission quota allocation and trading, and carbon trading prices to establish a green electricity-carbon market linkage model; S3, Green power transaction matching calculation: Based on user demand and supply capacity, combined with price factors, calculate the optimal green power transaction matching plan to achieve optimal cost and benefit; S4, Risk Assessment and Control: Assess the risks of the transaction plan, including market, regulatory, and technical risks, and formulate risk control measures; S5, Transaction Execution and Monitoring: Execute transactions according to the transaction plan and monitor in real time, collect and analyze data, and dynamically adjust the optimization plan.

2. A green electricity transaction matching method based on green electricity-carbon market linkage according to claim 1, characterized in that: The data collection and preprocessing in S1 include: S11, data collection: collecting green electricity supply prices, user electricity demand, carbon emission quotas and carbon trading prices; S12, data cleaning and screening: check the abnormal values ​​of green electricity transaction data, and remove or correct any abnormal data found; S13, data preprocessing: data standardization, data smoothing and dimension reduction processing are performed on the collected green electricity transaction data; The data smoothing adopts moving average method for smoothing; The dimension reduction process performs data dimension reduction by using a principal component analysis method.

3. A green electricity transaction matching method based on green electricity-carbon market linkage according to claim 2, characterized in that: The establishment of the green electricity-carbon market linkage model in S2 includes: S21, model building: calculate the green electricity supply cost, and build a green electricity-carbon market linkage model through the linkage mechanism between green electricity price and carbon trading price; S22, consider uncertainty factors: build a risk adjustment model by analyzing the standard deviation of price fluctuations and risk sensitivity coefficient; S23, price correction: introduce seasonal factors, regulatory factors and market competition factors to correct the green electricity price and carbon trading price.

4. A green electricity transaction matching method based on green electricity-carbon market linkage according to claim 3, characterized in that: The construction model in S21 includes: S211, cost calculation: Calculate the green electricity supply cost, expressed as: Among them, C g0 is the fixed cost, k1, k2, k3, k4 are cost coefficients, Q g is the green electricity supply, E g Energy conversion efficiency for green electricity supply, T g is the time factor of green electricity supply, R g Reliability indicators for green electricity supply; S212, Model establishment: Based on the green electricity supply cost C g , combined with the electricity demand of power users D u , Power purchase cost C u 、Allocation of Carbon Emission Quota A c and trading and carbon trading price P c A green electricity-carbon market linkage model is established, which can be expressed as: F(C g ,D u ,C u ,A c ,P c )=0; S213, Establishing a linkage mechanism: Constructing a green electricity price P g and carbon trading price P c The association mechanism is expressed as: Among them, k5, b1, k6, and k7 are correlation coefficients.

5. A green electricity transaction matching method based on green electricity-carbon market linkage according to claim 4, characterized in that: The risk adjustment model in S22 is expressed as: P′ g =P g (1+α1σ g ); P′ c =P c (1+α2σ c ); Among them, P1 and P2 are risk sensitivity coefficients, σ g and σ c are the standard deviations of price fluctuations of green electricity and carbon trading, P′ g and P′ c They are the adjusted green electricity price and carbon trading price respectively.

6. A green electricity transaction matching method based on green electricity-carbon market linkage according to claim 5, characterized in that: The price modification in S23 is expressed as: P″ g =P′ g (1+β1S season )(1+β2P policy )(1+03M competiton ); P″ c =P′ c (1+γ1S season )(1+γ2P policy )(1+γ3M competiton ); Among them, P g and P″ c are the revised green electricity price and carbon trading price respectively, β1, β2, β3, γ1, γ2, γ3 are the corresponding impact coefficients, S season , P policy 、M competiton They are seasonal factors, regulatory factors, and market competition factors.

7. A green electricity transaction matching method based on green electricity-carbon market linkage according to claim 6, characterized in that: The green electricity transaction matching calculation in S3 includes: S31, define the objective function: according to the power demand D of the power user u and the supply capacity of green electricity suppliers Q g , combined with the green electricity price P g and carbon trading price P c , the optimal green electricity trading matching scheme is calculated through the optimization algorithm, so that the objective function can obtain the minimum value under the constraints. The objective function is expressed as: Z=min(C g +C u +λ1R g +λ2R c +λ3E env ); Among them, λ1 and λ2 are risk weight coefficients, λ3 is the environmental weight coefficient, and E env for environmental benefits; S32, Constraints: Define constraints, expressed as: Q g ≥D u ; C u ≤C u,max ; A c ≥A c,min ; P g ∈(P g,min ,P g,max ); P c ∈(P c,min ,P c,max ); AND env ≥E env,min ; S soc ≥S soc,min ; P policy (Q g ,A c ,P c )≤0; Among them, P policy is the specified constraint function, k s , b2 is the market equilibrium coefficient.

8. A green electricity transaction matching method based on green electricity-carbon market linkage according to claim 7, characterized in that: The risk assessment and control in S4 include: S41, Market risk: Market risk includes green electricity price fluctuation risk, carbon trading price fluctuation risk and green electricity supply interruption risk, expressed as: ΔP g =P g (t1)-P g (t0); ΔP c =P c (t1)-P c (t0); Among them, ΔP g and ΔP c are the green electricity fluctuation price and carbon trading fluctuation price, P g (t1) and P c (t1) are the current green electricity price and the current carbon trading price, respectively. g (t0) and P c (t0) are the green electricity price and carbon trading price in the previous time period, ω g and ω c They are green electricity price volatility and carbon trading price volatility respectively; S42, Regulatory Risk: Evaluate and calculate the impact of changes in carbon emission regulations based on regulatory parameters, enforcement strength and timeliness, expressed as: E p =f2(P p ,L p ,T p ); Among them, P p is the specified parameter, L p For the enforcement strength of the regulations, T p For the timeliness of the provisions; S43, Technical Risk: By analyzing the green electricity supply, energy conversion efficiency, the covariance of supply and conversion efficiency, and reliability indicators, the stability risk of green electricity supply is evaluated, expressed as: S g =β1V ar (Q s )+β2V ar (E g )+β3C ov (Q g ,E g )+β4V ar (R g ); Among them, β1, β2, β3, β4 are stability weight coefficients, V ar represents the variance, C ov represents covariance; S44, Risk Control Strategy: Formulate corresponding risk control strategies, including price fluctuation warning, establishment of risk reserve and risk hedging strategies; The price fluctuation warning sets a price fluctuation warning threshold. When the green electricity price or carbon trading price exceeds the predetermined threshold, corresponding measures are taken, including adjusting the trading strategy or performing risk hedging, which is expressed as: |ΔP g |>θ g or |ΔP c |>θ c ; Among them, θ g and θ c is the warning threshold; The risk reserve fund is established by establishing a risk reserve fund model to provide financial support when risk events occur, which is expressed as: R reserve =k 10 (|ΔP g |+|ΔP c |+|E p |+|S g |+|I g |); Among them, k 10 is the reserve factor; The risk hedging strategy hedges price risk through futures contracts or options contracts, which can be expressed as: F futures =k 11 (P g,expected -P g,actual ); O options =k 12 max(0,P g,expected -P g,actual ); Among them, F futures is the futures contract value, O options is the option contract value, k 11 , k 12 is the contract coefficient.

9. A green electricity transaction matching method based on green electricity-carbon market linkage according to claim 8, characterized in that: The transaction execution and monitoring in S5 includes: S51, real-time monitoring: real-time monitoring of the transaction process, collecting the actual supply of green electricity Q g,actual , power consumption D u,actual and the transaction price execution g,executed ; S52, Deviation Analysis: Deviation analysis is performed on the difference between the actual monitoring data and the expected data to identify the source and size of the deviation in the transaction process, expressed as: S53, Transaction efficiency index calculation: By setting the transaction efficiency index, the efficiency and effect of the entire transaction process are measured and the quality of transaction execution is evaluated, which is expressed as: S54, plan adjustment: adjust the plan based on the monitoring results, including optimizing the trading plan, applying adaptive control algorithms, and using machine learning algorithms to assist decision making.

10. A green electricity transaction matching method based on green electricity-carbon market linkage according to claim 9, characterized in that: The scheme adjustment in S54 includes: S541, optimize the transaction plan: according to the results of real-time monitoring and deviation analysis, when the actual supply of green electricity, electricity consumption, transaction price execution and transaction efficiency exceed the preset threshold, the plan is optimized, which is expressed as: |Q g,actual -Q g |>∈ q ; |D u,actual -D u |>∈ d ; |P g,executed -P g |>∈ p ; E trans <∈ e ； S542, Adaptive control algorithm application: Use the adaptive control algorithm to dynamically adjust the trading plan to adapt to real-time changes, expressed as: Adjustment=f3(Deviation g ,Deviation u ,Deviation p ,E trans ,HistoricalDate); S543, machine learning algorithm assisted decision-making: using neural networks to analyze and predict monitoring data, and discover potential problems and risks in advance, expressed as: O = f4(WX+b); Among them, W is the weight matrix, X is the input data, b is the bias term, and f4 is the activation function.

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