Method, system, device and medium for evaluating and balancing green electricity competitiveness within and outside a province

Abstract

The present application relates to the field of green electricity technology, and particularly relates to a method, system, device and medium for evaluating and balancing the competitiveness of green electricity within and outside a province, the method comprising: obtaining green electricity related data of a target province and candidate supply provinces; preprocessing the green electricity related data to generate time series data sets for calculation; based on the time series data sets, combining power flow constraints and power transmission loss models of power transmission channels, calculating the available effective amount of green electricity and the green electricity demand of the target province in each time period to obtain a time-sharing supply and demand balance degree; determining the upper and lower limits of the time-sharing supply and demand balance degree based on historical operation data or a set confidence quantile, and taking the upper and lower limits as domain constraints; calculating the comprehensive closeness of each candidate supply source according to a preset index and weight, and using an improved TOPSIS, and decomposing a reference transaction price based on the comprehensive closeness; and sending the reference transaction price, the comprehensive closeness of each candidate supply source and a recommended transaction volume to a target province transaction platform or a dispatching unit.

Description

Technical Field

[0001] This invention relates to the field of green energy technology, and in particular to a method, system, equipment and medium for evaluating and balancing the competitiveness of green energy within and outside a province. Background Technology

[0002] Against the backdrop of increasingly stringent constraints on global warming and greenhouse gas emission reduction, countries are accelerating their green and low-carbon energy transition. Green electricity generation capacity and output, represented by renewable energy, are growing rapidly, the scale of green electricity trading is expanding, and the proportion of green electricity in final energy consumption is steadily increasing. Significant differences exist among provinces in terms of load levels, energy resource endowments, and power structure: one type is load-heavy provinces with high levels of economic development, large electricity demand, and relatively insufficient local renewable energy resources; the other type is resource-rich provinces with excellent wind, solar, or hydropower resources and strong renewable energy generation capacity, but relatively limited local load. To achieve the national goal of green and low-carbon development, provinces are forming an "inter-provincial green electricity collaborative supply" pattern through inter-provincial power transmission channels, with load-heavy provinces absorbing a large amount of green electricity from resource-rich provinces.

[0003] However, in inter-provincial green electricity trading, the lack of a price balancing mechanism based on the value of carbon emission reduction and the differences in emission factors of provincial power systems makes it difficult to accurately reflect the true environmental value of green electricity from different receiving provinces and its contribution to the supply and demand of the receiving province's power system, which has become a key bottleneck restricting the further development of the green electricity market.

[0004] The information disclosed in this background section is intended only to enhance the understanding of the general background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] This invention provides a method, system, equipment, and medium for evaluating and balancing the competitiveness of green electricity within and outside a province, thereby effectively solving the problems in the background art.

[0006] To achieve the above objectives, the technical solution adopted by this invention is: a method for evaluating and balancing the competitiveness of green electricity within and outside a province, comprising the following steps: Obtain green electricity-related data from the target province and candidate supply provinces. The green electricity-related data shall include at least supply-side data, cost and price data, environmental and carbon emission data, and policy and market environment data. The green electricity-related data is preprocessed to generate a time-series dataset for computation. Based on the time-series dataset, combined with the power flow constraints and transmission loss model of the transmission channel, the available green electricity supply and the green electricity demand of the target province are calculated for each time period to obtain the time-sharing supply and demand balance. Based on the supply-side data setting ratio, or based on the confidence quantile set by the sample statistical distribution, the upper and lower limits of the time-sharing supply and demand balance are determined, and the upper and lower limits are used as domain constraints. Based on preset indicators and weights, and using an improved TOPSIS, the comprehensive proximity of each candidate supply source is calculated, and the cost and price data are decomposed based on the comprehensive proximity to form a reference transaction price. The reference transaction price, the overall proximity ranking of each candidate supply source, and the suggested transaction volume are sent to the target province's transaction platform or scheduling unit.

[0007] Furthermore, the preprocessing of the green electricity-related data includes: handling missing values, removing outliers, and normalizing units and time.

[0008] Furthermore, by combining the power flow constraints and transmission loss model of the transmission channel, the effective amount of available green electricity and the green electricity demand of the target province are calculated for each time period to obtain the time-sharing supply-demand balance, including: Define the green electricity demand within the selected time scale, taking the target province as the object; For each candidate supply source, the effective amount of green electricity that can be actually supplied to the target province and its temporal distribution are calculated under the conditions of safe grid operation and channel constraints. Define the time-sharing supply and demand balance B of province i in time period t as: ; In the formula, The available and effective green electricity; The green electricity demand of the target province is represented by the subscript i, which represents the target province, and the subscript t represents the time.

[0009] Furthermore, determining the upper and lower limits of the time-sharing supply-demand balance based on historical operating data or set confidence quantiles, and using these upper and lower limits as domain constraints, includes: The time-sharing supply and demand balance is mapped to the upper and lower limits [B] using a truncation or nonlinear mapping method. min B max Within the interval; B min With B max This is a threshold parameter for supply and demand balance, used to suppress the irrational impact of extreme supply and demand imbalance samples on the comprehensive evaluation; Set the allowable supply deficit ratio r low The proportion of supply surplus allowed r high Then B min =1-r low B max =1+r high .

[0010] Furthermore, the preset indicators and weights include: Indicators related to supply capacity and absorption conditions: installed capacity of new energy sources; annual power generation and utilization hours; capacity and constraints of inter-provincial power transmission channels; supply and demand balance (B); Price and cost competitiveness indicators: cost per kilowatt-hour of power generation; transmission and distribution costs and channel occupancy costs; electricity market prices during typical periods; existing green electricity transaction prices and their price difference with conventional power sources; Carbon reduction benefits and environmental value indicators: provincial power system mixed emission factor level; unit emission reduction of green electricity replacing benchmark electricity; total emission reduction potential under a given green electricity trading scale; Policy and market environment indicators: green electricity consumption constraints and their completion status; the degree of perfection of the green certificate issuance and confirmation mechanism; the degree of marketization and the flexibility of trading rules; The indicators are divided into positive and negative indicators; the indicators are dimensionless by using extreme value standardization and interval standardization methods to unify the direction of the indicators. The Analytic Hierarchy Process (AHP) was used to construct a judgment matrix, and the subjective weights of each indicator were calculated by integrating expert experience and policy guidance. The entropy weight method is used to calculate the objective weight of each indicator based on the dispersion of indicator data in different provinces or power sources. The subjective and objective weights are linearly combined according to a preset ratio to obtain the final combined weight vector.

[0011] Furthermore, the calculation of the overall proximity of each candidate supply source using the improved TOPSIS includes: Construct a weighted standardized matrix V=[v] based on the preset indicators and weights. ij ] m×n Where m is the number of evaluation objects and n is the number of evaluation indicators, and: ; In the formula, z ij w is the standardized value of the i-th evaluation object on the j-th indicator. j Let be the combined weight of the j-th indicator; To determine the positive ideal solution vector of TOPSIS With negative ideal solution vector First, the evaluation indicators are divided into benefit-type indicators, where a larger indicator value is better, and cost-type indicators, where a smaller indicator value is better; for the j-th indicator, the positive ideal component... With negative ideal component Determined according to the following rules: If the j-th indicator is a benefit-type indicator: ; If the j-th indicator is a cost-type indicator: ; Based on this, we can obtain and : ; Calculate the distance of each evaluation object to the positive and negative ideal solutions; Calculate the overall similarity C i : ; In the formula, To evaluate the distance between object i and the positive ideal solution, This is the distance from the negative ideal solution.

[0012] Furthermore, the reference transaction price P based on the comprehensive proximity decomposition... g ,include: ; In the formula, It serves as a benchmark price for electricity, linked to the medium- and long-term market price of electricity or the benchmark on-grid price in the target province. This is the environmental value difference, used to reflect the differences in the emission reduction effects of green electricity from different provinces. This is a supply and demand adjustment item used to reflect the tightness of green electricity supply and demand and the market conditions in the target province.

[0013] Furthermore, the calculation of the environmental value difference incorporates the deduction rules for the confirmed green electricity share from the carbon registration system or the green certificate registration system, and obtains the confirmed share and corresponding emission factor data by reading the registration system in real time via API.

[0014] Furthermore, the suggested transaction volume includes: Based on the comprehensive proximity and the economic constraints of both supply and demand, several suggested combinations of electricity transactions are automatically generated, and the implementation cost and emission reduction potential of each combination are assessed for decision-makers to choose from.

[0015] This invention also includes a system for assessing and balancing the competitiveness of green electricity within and outside the province, using the method described above. The system comprises: The data acquisition unit is used to acquire green electricity-related data of the target province and candidate supply provinces. The green electricity-related data includes at least supply-side data, cost and price data, environmental and carbon emission data, and policy and market environment data. A preprocessing unit is used to preprocess the green electricity-related data to generate a time-series dataset for calculation. The supply and demand balance unit is used to calculate the available green electricity supply and the target province's green electricity demand for each time period based on the time series dataset, combined with the power flow constraints and transmission loss model of the transmission channel, to obtain the time-sharing supply and demand balance. The upper and lower limit units are used to set the ratio based on the supply-side data or to determine the upper and lower limits of the time-sharing supply and demand balance based on the confidence quantile set based on the sample statistical distribution, and to use the upper and lower limits as domain constraints. The evaluation unit is used to calculate the overall proximity of each candidate supply source according to preset indicators and weights and using an improved TOPSIS, and to decompose the cost and price data based on the overall proximity to form a reference transaction price. The balancing unit is used to send the reference transaction price, the comprehensive proximity ranking of each candidate supply source, and the suggested transaction volume to the target province's transaction platform or scheduling unit.

[0016] The present invention also includes a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described above.

[0017] The present invention also includes a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described above.

[0018] The beneficial effects of this invention are as follows: by constructing a multi-dimensional indicator system and using improved TOPSIS and other methods, the comprehensive competitiveness of different types of green electricity in this province and other provinces is quantitatively ranked, providing a scientific and explainable decision-making basis for load-type provinces to formulate green electricity procurement strategies. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the method for evaluating and balancing the competitiveness of green electricity within and outside the province in this embodiment. Figure 2 This is a flowchart of the system construction method in the embodiment; Figure 3 This is a schematic diagram of the supply and demand balance results in the example. Figure 4 This is a schematic diagram of the TOPSIS competitiveness ranking in the embodiment; Figure 5This is a schematic diagram illustrating the breakdown of green electricity prices in the example. Figure 6 This is a schematic diagram of carbon price sensitivity analysis in the examples; Figure 7 This is a schematic diagram of the system structure in the embodiment; Figure 8 This is a schematic diagram of the structure of the computer device of the present invention. Detailed Implementation

[0021] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0022] like Figures 1 to 5 The following is a method for assessing and balancing the competitiveness of green electricity within and outside the province, comprising the following steps: Obtain green electricity-related data from the target province and candidate supply provinces. The green electricity-related data should include at least supply-side data, cost and price data, environmental and carbon emission data, and policy and market environment data. Preprocess the green electricity-related data to generate a time-series dataset for computation; Based on time-series datasets, combined with power flow constraints and transmission loss models of transmission channels, the available green electricity supply and the green electricity demand of the target province are calculated for each time period to obtain the time-sharing supply and demand balance. The upper and lower limits of the time-sharing supply and demand balance are determined based on the proportion set by the supply-side data or the confidence quantile set by the sample statistical distribution, and the upper and lower limits are used as domain constraints. Based on preset indicators and weights, and using an improved TOPSIS to calculate the overall proximity of each candidate supply source, and decompose cost and price data based on the overall proximity to form a reference transaction price; The reference transaction price, the overall proximity ranking of each candidate supply source, and the suggested transaction volume will be sent to the target province's transaction platform or scheduling unit.

[0023] By constructing a multi-dimensional indicator system and adopting improved TOPSIS and other methods, the comprehensive competitiveness of different types of green electricity in this province and other provinces is quantitatively ranked, providing a scientific and explainable decision-making basis for load-type provinces to formulate green electricity procurement strategies.

[0024] Specifically, obtain green electricity-related data from the target province and candidate supply provinces. This green electricity-related data should include at least supply-side data, cost and price data, environmental and carbon emission data, and policy and market environment data, including but not limited to: Supply-side data: installed capacity of new energy sources such as wind power and photovoltaics; annual power generation and utilization hours; adjustable output level; capacity of inter-provincial power transmission channels, transmission losses and operational constraints.

[0025] Cost and price data: generation cost per kilowatt-hour for different power sources; transmission and distribution prices and channel occupancy costs; historical electricity trading prices; existing green electricity trading prices and price differences; green certificate prices, etc.

[0026] Environmental and carbon emission data: Mixed emission factors of power systems in each province (such as RMF, AEF, etc. Among them, RMF is the residual mixed emission factor, used to characterize the carbon emission intensity per unit of electricity in the remaining electricity structure of the power system after deducting specific clean electricity such as green electricity with confirmed rights; AEF is the average emission factor, used to characterize the average carbon emission intensity per unit of electricity supplied by the power grid to users within the statistical period); unit emission reduction corresponding to different green electricity products; total emission reduction potential under the corresponding transaction scale.

[0027] Policy and market environment data: green electricity consumption targets and quota assessment rules for each province; subsidy and incentive policies; degree of electricity marketization, trading varieties and terms, etc.

[0028] The green electricity-related data is preprocessed, including missing value handling, outlier removal, and unit and time normalization, forming a data matrix of "province or power type - evaluation index", which is the time series dataset used for calculation.

[0029] In this embodiment, by combining the power flow constraints and transmission loss model of the transmission channel, the available green electricity supply and the target province's green electricity demand are calculated for each time period to obtain the time-sharing supply-demand balance, including: The green electricity demand within the selected time scale is defined, taking the target province as the target; including: the minimum green electricity ratio demand determined according to policy objectives; and the additional green electricity demand voluntarily proposed by electricity-consuming enterprises. For each candidate supply source, the effective amount of green electricity that can be actually supplied to the target province and its temporal distribution are calculated under the conditions of safe grid operation and channel constraints. Define the time-sharing supply and demand balance B of province i in time period t as: ; In the formula, The available and effective green electricity; The green electricity demand of the target province is represented by the subscript i, which represents the target province, and the subscript t represents the time.

[0030] To avoid situations where solutions with extreme oversupply or extreme shortage unreasonably receive high scores in the comprehensive evaluation, a proportion is set based on the aforementioned supply-side data, or a confidence quantile is set based on the sample statistical distribution to determine the upper and lower limits of the time-sharing supply-demand balance, and these upper and lower limits are used as domain constraints, including: The time-sharing supply and demand balance is mapped to upper and lower limits using truncation or nonlinear mapping methods [B]. min B maxWithin the specified range; the supply-demand balance is included as an independent indicator in the comprehensive evaluation system to characterize the absorption capacity of a particular green energy source in the target province; B min With B max This is a threshold parameter for supply and demand balance, used to suppress the irrational impact of extreme supply and demand imbalance samples on the comprehensive evaluation; Set the allowable supply deficit ratio r low The proportion of supply surplus allowed r high Then B min =1-r low B max =1+r high ; Alternatively, based on the statistical distribution of B using historical / simulated samples, the quantile method can be used to determine B. min With B max .

[0031] The preset indicators and weights include: Indicators related to supply capacity and absorption conditions: installed capacity of new energy sources; annual power generation and utilization hours; capacity and constraints of inter-provincial power transmission channels; supply and demand balance (B); Price and cost competitiveness indicators: cost per kilowatt-hour of power generation; transmission and distribution costs and channel occupancy costs; electricity market prices during typical periods; existing green electricity transaction prices and their price difference with conventional power sources; Carbon reduction benefits and environmental value indicators: provincial power system mixed emission factor level; unit emission reduction of green electricity replacing benchmark electricity; total emission reduction potential under a given green electricity trading scale; Policy and market environment indicators: green electricity consumption constraints and their completion status; the degree of perfection of the green certificate issuance and confirmation mechanism; the degree of marketization and the flexibility of trading rules; Indicator standardization and combination weight determination include: Indicator standardization: The indicators are divided into positive and negative indicators; the indicators are dimensionless by using extreme value standardization and interval standardization methods to unify the direction of the indicators. Determining the combined weights: The Analytic Hierarchy Process (AHP) was used to construct a judgment matrix, and the subjective weights of each indicator were calculated by integrating expert experience and policy guidance. The entropy weight method is used to calculate the objective weight of each indicator based on the dispersion of indicator data in different provinces or power sources. The subjective and objective weights are linearly combined according to a preset ratio to obtain the final combined weight vector.

[0032] As a preferred embodiment of the above, an improved TOPSIS is used to calculate the overall proximity of each candidate supply source, including: Construct a weighted standardized matrix V=[v] based on preset indicators and weights. ij ] m×n Where m is the number of evaluation objects and n is the number of evaluation indicators, and: ; In the formula, z ij w is the standardized value of the i-th evaluation object on the j-th indicator. j Let be the combined weight of the j-th indicator; Among them, determining the standardization matrix requires determining the positive ideal solution and the negative ideal solution, and introducing upper and lower limit constraints on key indicators such as the degree of supply and demand balance in order to avoid the influence of extreme values; To determine the positive ideal solution vector of TOPSIS With negative ideal solution vector First, the evaluation indicators are divided into benefit-type indicators, where a larger indicator value is better, and cost-type indicators, where a smaller indicator value is better; for the j-th indicator, the positive ideal component... With negative ideal component Determined according to the following rules: If the j-th indicator is a benefit-type indicator: ; If the j-th indicator is a cost-type indicator: ; Based on this, we can obtain and : ; Calculate the distance of each evaluation object to the positive and negative ideal solutions; Calculate the overall similarity C i : ; In the formula, To evaluate the distance between object i and the positive ideal solution, This is the distance from the negative ideal solution.

[0033] After obtaining the green electricity competitiveness evaluation results (including the comprehensive proximity Ci of each green electricity supply source and its ranking), the ranking results are used as input to the price balancing mechanism to determine the set of supply sources participating in green electricity transactions within and outside the province and their procurement priorities, and can generate suggested transaction volumes or combination schemes accordingly. On this basis, the bid price or transaction reference price of each supply source is calculated according to the decomposition framework of electricity benchmark price, environmental value price difference and supply and demand adjustment items, so as to achieve the unity of price and competitiveness, environmental value and supply and demand structure. Specifically, this includes: price decomposition framework construction, environmental value price difference calculation, supply and demand adjustment item design, multi-market coordination and product design.

[0034] In this embodiment, the reference transaction price P is decomposed based on the comprehensive proximity. g ,include: ; In the formula, It serves as a benchmark price for electricity, linked to the medium- and long-term market price of electricity or the benchmark on-grid price in the target province. This is the environmental value difference, used to reflect the differences in the emission reduction effects of green electricity from different provinces. This is a supply and demand adjustment item used to reflect the tightness of green electricity supply and demand and the market conditions in the target province.

[0035] This decomposition framework aims to reveal the composition of value, providing a transparent and scientific analytical basis for the formation of transaction prices. In actual transactions, buyers and sellers can determine prices according to market rules. For reference, a final "electricity price + environmental rights price" transaction contract is formed through negotiation or bidding. In this model... and This provides a differentiated and dynamic quantitative basis for pricing environmental rights.

[0036] The calculation of environmental value difference incorporates the deduction rules for the confirmed green electricity share from the carbon registration system or green certificate registration system, and obtains the confirmed share and corresponding emission factor data from the registration system in real time via API.

[0037] Specifically, it includes the following steps: Obtain the combined emission factor values ​​of the power system for the target province and each power supply province, and calculate the emission factor difference. : In the formula, and These represent the target and actual values ​​of the emission factor, respectively. Obtain a unified carbon price or reference carbon market price Based on the unit emission reduction corresponding to green electricity, the environmental value difference is defined as: If a green certificate market exists, the price of green certificates can be factored into the calculation. Alternatively, environmental values ​​can be measured separately to avoid double-counting.

[0038] Specifically, based on the green electricity supply and demand balance B of the target province, supply and demand adjustment items are designed. : When B min When green electricity is in short supply, set up >0, moderately raise the price of green electricity to guide more green electricity from other provinces to the target province; When B​min ≤B≤B max When, set >0, the price of green electricity is mainly determined by the difference between the benchmark price of electricity and the environmental value; When B>B max When green electricity is relatively abundant, set up <0, moderately reduce the price of green electricity to balance the development of green electricity with the affordability of electricity costs.

[0039] This can be achieved using piecewise linear functions or smooth nonlinear functions to ensure that green electricity prices adjust smoothly with changes in supply and demand. The specific methods are as follows: From the above formula, we can see that when B=1, =0; when the supply-demand deviation widens. Gradually approaching the upper / lower limits to achieve smooth adjustment.

[0040] Based on the above price decomposition, further provisions are made regarding multi-market collaboration and product design: It is compatible with existing medium- and long-term electricity contracts and spot market mechanisms, and supports green electricity contracts on multiple time scales such as annual, quarterly, and monthly. Support innovative trading products such as "green electricity + green certificates" combination products and environmental value options based on emission factor differences; At the carbon market level, the rules for calculating and deducting green electricity emission reductions in carbon quotas or voluntary emission reduction mechanisms should be clarified to avoid double counting and arbitrage opportunities.

[0041] As a preferred embodiment of the above, the suggested transaction volume includes: Based on comprehensive proximity and economic constraints of both supply and demand, several suggested combinations of electricity trading are automatically generated, and the implementation cost and emission reduction potential of each combination are assessed for decision-makers to choose from.

[0042] like Figure 6 As shown, this embodiment also includes a green energy competitiveness assessment and balancing system for provinces and regions, using the method described above. The system includes: The data acquisition unit is used to acquire green electricity-related data from the target province and candidate supply provinces. The green electricity-related data includes at least generator output, power flow and capacity utilization of transmission lines, transmission losses, load curves, system frequency, system voltage, historical and real-time electricity trading prices, green certificate records and carbon prices. The preprocessing unit is used to preprocess green electricity-related data to generate a time-series dataset for computation. The supply and demand balance unit is used to calculate the available green electricity supply and the target province's green electricity demand for each time period based on time-series datasets, combined with power flow constraints and transmission loss models of transmission channels, to obtain the time-sharing supply and demand balance. Upper and lower limit units are used to determine the upper and lower limits of the time-sharing supply and demand balance based on historical operating data or set confidence quantiles, and to use the upper and lower limits as domain constraints. The evaluation unit is used to calculate the overall proximity of each candidate supply source according to preset indicators and weights, and to decompose the reference transaction price based on the overall proximity. The balancing unit is used to send the reference transaction price, the overall proximity ranking of each candidate supply source, and the suggested transaction volume to the target province's transaction platform or scheduling unit.

[0043] Each of the above modules can be implemented by executing the corresponding software program through one or more processors, or by dedicated hardware or programmable logic devices. The modules communicate with each other through a bus or network.

[0044] This example uses provincial-level data as a case study, and the calculation results are as follows: Depend on Figure 3 The heat map shows significant differences in the time-of-use supply and demand matching of the three types of green electricity: For wind power within the province, the supply rates for t1-t4 are 0.79, 0.75, 1.06, and 0.85 respectively, with only a slight surplus (+6%) at t3, while the remaining periods are tight; for photovoltaic power within the province, the rates are 0.39, 0.71, 0.59, and 0.71, with insufficient supply in all four periods, and the largest gap at t1, covering only 39% of demand; for wind and solar power from other provinces, the rates are 1.14, 0.91, 1.42, and 1.14, generally showing a surplus, with the most significant surplus at t3 (+42%). (Combined with B) i The bar chart further illustrates the wind power B in this province. i =0.8488, Provincial Photovoltaic B i =0.5880, Out-of-province scenic call B i =1.1358, combined with reference line B min =0.75, B=1, B max =1.25 can be quantitatively determined: the supply of electricity from wind and solar power in other provinces is ample (B) i >1) The supply of wind power and photovoltaic power in this province is tight, especially photovoltaic power, which is lower than B. min This will directly determine the subsequent supply and demand adjustment item ΔP. bal The positive and negative values ​​and their magnitude.

[0045] Depend on Figure 4 It can be seen that the degree of closeness C iThe ranking is as follows: wind and solar power from other provinces first, wind power from within the province second, and solar power from within the province third. The advantage of the first-ranked power over the second-ranked power is 0.1732, and the advantage of the second-ranked power over the third-ranked power is 0.1558. This indicates that wind and solar power from other provinces is closer to the ideal solution under the set comprehensive indicators of "supply and demand matching, cost, emission reduction and environment, and policy and market," while solar power from within the province has the lowest overall closeness. Figure 1 In its comprehensive B i The significant supply-demand mismatch corroborates each other.

[0046] Depend on Figure 5 It can be seen that, under a unified benchmark electricity price P e At 380 yuan / MWh, the total price of the three types of green electricity is respectively P for wind and solar power from other provinces. g =374.9, wind power in this province P g =403.0, Provincial photovoltaic P g =421.4, the difference is mainly driven by supply and demand adjustment items and is consistent with Figure 2 B i Consistent direction: Environmental value difference ΔP between wind and solar power from other provinces env =12.32 yuan / MWh, but due to the surplus supply, a supply-demand discount ΔP occurs. bal =-17.39 yuan / MWh, the final total price is actually lower than P e Lower by 5.1 yuan / MWh; ΔP of wind power in this province env =3.92、ΔP bal =19.12, net premium approximately +23.0; ΔP of photovoltaic power in this province env =3.36, but due to the tightest supply and demand, a larger ΔP is formed. bal =37.99, with a net premium of approximately +41.4, thus quantitatively reflecting the superimposed mechanism of environmental value premium and supply and demand scarcity adjustment.

[0047] Depend on Figure 7 It can be seen that the slope of wind and solar power from other provinces is the largest at 0.154, while the slopes of wind power and solar power from within the province are 0.049 and 0.042, respectively; correspondingly, at P C When P=0, the intercepts for the three values ​​are 362.6, 399.1, and 418.0 yuan / MWh, respectively. C When the price increases from 0 to 150 yuan / tCO2, the total price increases to 385.7, 406.5, and 424.3 yuan / MWh respectively. The increase in the price of wind and solar power from other provinces is the largest, but it still remains the lowest in this range. This indicates that the carbon price signal will significantly increase the price elasticity of green electricity varieties with stronger emission reduction advantages, while not changing the price pattern of "highest price for local photovoltaic power and lowest price for wind and solar power from other provinces".

[0048] Please see Figure 8The diagram shows a structural schematic of a computer device provided in an embodiment of this application. An embodiment of this application provides a computer device 400, including a processor 410 and a memory 420. The memory 420 stores a computer program executable by the processor 410. When the computer program is executed by the processor 410, it performs the method described above.

[0049] This application embodiment also provides a storage medium 430, on which a computer program is stored, and the computer program is executed by a processor 410 to perform the above method.

[0050] The storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0051] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0052] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0053] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0054] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0055] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0056] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0057] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0058] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for evaluating and balancing the competitiveness of green electricity within and outside a province, characterized in that, Includes the following steps: Obtain green electricity-related data from the target province and candidate supply provinces. The green electricity-related data includes at least supply-side data, cost and price data, environmental and carbon emission data, and policy and market environment data. The green electricity-related data is preprocessed to generate a time-series dataset for computation. Based on the time-series dataset, combined with the power flow constraints and transmission loss model of the transmission channel, the available green electricity supply and the green electricity demand of the target province are calculated for each time period to obtain the time-sharing supply and demand balance. Based on the supply-side data, a ratio is set, or a confidence quantile is set based on the sample statistical distribution; the upper and lower limits of the time-sharing supply-demand balance are determined, and the upper and lower limits are used as domain constraints. Based on preset indicators and weights, and using an improved TOPSIS, the comprehensive proximity of each candidate supply source is calculated, and the cost and price data are decomposed based on the comprehensive proximity to form a reference transaction price. The reference transaction price, the overall proximity ranking of each candidate supply source, and the suggested transaction volume are sent to the target province's transaction platform or scheduling unit.

2. The method for evaluating and balancing the competitiveness of green electricity within and outside the province according to claim 1, characterized in that, The preprocessing of the green electricity-related data includes: handling missing values, removing outliers, and normalizing units and time.

3. The method for evaluating and balancing the competitiveness of green electricity within and outside the province according to claim 1, characterized in that, The method combines power flow constraints and transmission loss models of transmission channels to calculate the available green electricity supply and the target province's green electricity demand for each time period, thus obtaining the time-of-use supply-demand balance, including: Define the green electricity demand within the selected time scale, taking the target province as the object; For each candidate supply source, calculate the effective amount of green electricity that can be actually supplied to the target province and its temporal distribution under the conditions of safe grid operation and channel constraints; Define the time-sharing supply and demand balance B of province i in time period t as: ； In the formula, The available and effective green electricity; The green electricity demand of the target province is represented by the subscript i, which represents the target province, and the subscript t represents the time.

4. The method for evaluating and balancing the competitiveness of green electricity within and outside the province according to claim 1, characterized in that, The step of determining the upper and lower limits of the time-sharing supply-demand balance based on the proportion set based on the supply-side data, or based on the confidence quantiles set based on the sample statistical distribution, and using the upper and lower limits as domain constraints, includes: The time-sharing supply and demand balance is mapped to the upper and lower limits [B] using a truncation or nonlinear mapping method. min B max Within the interval; B min With B max This is a threshold parameter for supply and demand balance, used to suppress the irrational impact of extreme supply and demand imbalance samples on the comprehensive evaluation; Set the allowable supply deficit ratio r low The proportion of allowable oversupply r high Then B min =1-r low B max =1+r high .

5. The method for evaluating and balancing the competitiveness of green electricity within and outside the province according to claim 1, characterized in that, The preset indicators and weights include: Indicators related to supply capacity and absorption conditions: installed capacity of new energy sources; annual power generation and utilization hours; capacity and constraints of inter-provincial power transmission channels; supply and demand balance (B); Price and cost competitiveness indicators: cost per kilowatt-hour of power generation; transmission and distribution costs and channel occupancy costs; electricity market prices during typical periods; existing green electricity transaction prices and their price difference with conventional power sources; Carbon reduction benefits and environmental value indicators: provincial power system mixed emission factor level; unit emission reduction of green electricity replacing benchmark electricity; total emission reduction potential under a given green electricity trading scale; Policy and market environment indicators: green electricity consumption constraints and their completion status; the degree of perfection of the green certificate issuance and confirmation mechanism; the degree of marketization and the flexibility of trading rules; The indicators are divided into positive and negative indicators; the indicators are dimensionless by using extreme value standardization and interval standardization methods to unify the direction of the indicators. The Analytic Hierarchy Process (AHP) was used to construct a judgment matrix, and the subjective weights of each indicator were calculated by integrating expert experience and policy guidance. The entropy weight method is used to calculate the objective weight of each indicator based on the dispersion of indicator data in different provinces or power sources. The subjective and objective weights are linearly combined according to a preset ratio to obtain the final combined weight vector.

6. The method for evaluating and balancing the competitiveness of green electricity within and outside the province according to claim 5, characterized in that, The calculation of the overall proximity of each candidate supply source using the improved TOPSIS includes: Construct a weighted normalization matrix V=[v] based on the preset constraints and weights. ij ] m×n Where m is the number of evaluation objects and n is the number of evaluation indicators, and: ； In the formula, z ij w is the standardized value of the i-th evaluation object on the j-th indicator. j Let be the combined weight of the j-th indicator; This represents the weighted standardized value of the i-th evaluation object on the j-th indicator; To determine the positive ideal solution vector of TOPSIS With negative ideal solution vector First, the evaluation indicators are divided into benefit-type indicators, where a larger indicator value is better, and cost-type indicators, where a smaller indicator value is better; for the j-th indicator, the positive ideal component... With negative ideal component Determined according to the following rules: If the j-th indicator is a benefit-type indicator: ； If the j-th indicator is a cost-type indicator: ； Based on this, we can obtain and : ； Calculate the distance of each evaluation object to the positive and negative ideal solutions; Calculate the overall similarity C i : ； In the formula, To evaluate the distance between object i and the positive ideal solution, This is the distance from the negative ideal solution.

7. The method for evaluating and balancing the competitiveness of green electricity within and outside the province according to claim 5, characterized in that, The cost and price data are decomposed based on the comprehensive proximity to form a reference transaction price P. g ,include: ； In the formula, It serves as a benchmark price for electricity, linked to the medium- and long-term market price of electricity or the benchmark on-grid price in the target province. This is the environmental value difference, used to reflect the differences in the emission reduction effects of green electricity from different provinces. This is a supply and demand adjustment item used to reflect the tightness of green electricity supply and demand and the market conditions in the target province.

8. The method for evaluating and balancing the competitiveness of green electricity within and outside the province according to claim 7, characterized in that, The calculation of the environmental value difference incorporates the deduction rules for the confirmed green electricity share from the carbon registration system or green certificate registration system, and obtains the confirmed share and corresponding emission factor data by reading the registration system in real time via API.

9. The method for evaluating and balancing the competitiveness of green electricity within and outside the province according to claim 1, characterized in that, The recommended trading volume includes: Based on the comprehensive proximity and the economic constraints of both supply and demand, several suggested combinations of electricity transactions are automatically generated, and the implementation cost and emission reduction potential of each combination are assessed for decision-makers to choose from.

10. A system for evaluating and balancing the competitiveness of green electricity within and outside a province, characterized in that, Using the method of any one of claims 1 to 9, the system comprises: The data acquisition unit is used to acquire green electricity-related data of the target province and candidate supply provinces. The green electricity-related data includes at least supply-side data, cost and price data, environmental and carbon emission data, and policy and market environment data. A preprocessing unit is used to preprocess the green electricity-related data to generate a time-series dataset for calculation. The supply and demand balance unit is used to calculate the available green electricity supply and the target province's green electricity demand for each time period based on the time series dataset, combined with the power flow constraints and transmission loss model of the transmission channel, to obtain the time-sharing supply and demand balance. The upper and lower limit units are used to determine the upper and lower limits of the time-sharing supply and demand balance based on historical operating data or set confidence quantiles, and to use the upper and lower limits as domain constraints. The evaluation unit is used to calculate the overall proximity of each candidate supply source according to preset indicators and weights, and using an improved TOPSIS, and to decompose the reference transaction price based on the overall proximity. The balancing unit is used to send the reference transaction price, the comprehensive proximity ranking of each candidate supply source, and the suggested transaction volume to the target province's transaction platform or scheduling unit.

11. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1-9.

12. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method as described in any one of claims 1-9.

Images