Electricity transaction method and system based on random clearing mode

Abstract

The invention is suitable for the technical field of random clearing, and provides a power transaction method and system based on a random clearing mode, and the method comprises the steps: obtaining historical power supply and demand data, carbon emission data and supply and demand data monitored in real time of a preset region, and carrying out the power supply and demand analysis of the obtained data; determining a random distribution model of the power price of the preset region based on the power supply-demand analysis result; obtaining power related data of power generation enterprises and power utilization enterprises in a preset region, and determining a range distribution model of power distribution according to the obtained data; determining a random clearing model according to the random distribution model of the power price and the range distribution model of the power distribution, and carrying out clearing calculation and optimization based on the random clearing model; the transaction result is determined according to the clearing calculation and optimization, and the electric charge settlement is performed according to the bid-winning electricity price and the actual electricity consumption in the transaction result, so that the accuracy of the electric charge settlement based on the random clearing condition is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of random clearing technology, and in particular to an electricity trading method and system based on a random clearing mode. Background Art

[0002] With the continuous advancement of power market reform and the widespread access to distributed energy, the complexity and uncertainty of the power system are increasing. On the one hand, the output of distributed energy such as distributed photovoltaics and small wind power is affected by factors such as environmental weather, and has strong randomness and volatility, which brings challenges to the balance of power supply and demand. On the other hand, the electricity consumption behavior of power users is becoming more and more diversified, such as the charging load of electric vehicles and the peak and valley changes of residential electricity consumption, making load forecasting more difficult.

[0003] In the traditional electricity trading clearing model, transactions are often matched based on deterministic load forecasts and power generation plans, which makes it difficult to adapt to the increasingly prominent random factors in the power system. Due to uncertain factors such as the intermittent and volatile nature of renewable energy generation, power generation companies cannot accurately reflect their true power generation costs and risks. Some power generation companies may gain bidding advantages due to accidental factors, while other companies may lose fair competition opportunities due to the neglect of uncertainties even if their power generation costs are high but their technology is reliable, thus destroying the fair competition environment in the market. In addition, power users cannot obtain sufficient power supply when there is a power shortage, but still have to pay fees at a fixed price; when there is a power surplus, power generation companies may face the situation of selling power at a low price, resulting in uneven distribution of benefits among market participants and causing unfair trading markets. Summary of the invention

[0004] The embodiment of the present application provides an electricity trading method based on a random clearing mode, which is used to solve the problem of unfairness in the trading market caused by some uncertain factors.

[0005] A first aspect of an embodiment of the present application provides an electricity trading method based on a random clearing mode, comprising:

[0006] Obtain historical power supply and demand data, carbon emission data and real-time monitored supply and demand data for a preset area, and use the acquired data to conduct power supply and demand analysis;

[0007] Determine a random distribution model of electricity prices in the preset area based on the power supply and demand analysis results;

[0008] Obtaining power-related data of power generation enterprises and power consumption enterprises in the preset area, and determining a range distribution model of power distribution based on the acquired data;

[0009] Determine a stochastic clearing model based on the stochastic distribution model of the electricity price and the range distribution model of the electricity quantity allocation, and perform clearing calculation and optimization based on the stochastic clearing model;

[0010] Determine the transaction result according to the clearing calculation and optimization, and perform electricity bill settlement according to the winning bid electricity price and the actual electricity consumption in the transaction result.

[0011] Furthermore, the determining the stochastic distribution model of the electricity price in the preset area based on the electricity supply and demand analysis result includes:

[0012] Determine the probability distribution model and parameters of the preset area according to the electricity supply and demand analysis result;

[0013] Update the model parameters using the real-time monitored data to obtain the updated stochastic distribution model.

[0014] Furthermore, the obtaining the electricity-related data of the power generation enterprises and power consumption enterprises in the preset area and determining the range distribution model of the electricity quantity allocation according to the obtained data includes:

[0015] Determine the stochastic range of electricity quantity allocation for power generation enterprises and the stochastic range of electricity quantity allocation for power consumption enterprises respectively;

[0016] Adjust the stochastic range of electricity quantity allocation for the power generation enterprises or the stochastic range of electricity quantity allocation for the power consumption enterprises based on factors such as unit reliability, carbon emission factors, and government authorized contracts.

[0017] Furthermore, the determining the stochastic range of electricity quantity allocation for power generation enterprises and the stochastic range of electricity quantity allocation for power consumption enterprises respectively includes:

[0018] The expression of the stochastic range of electricity quantity allocation for power generation enterprises includes:

[0019]

[0020] Where: and are respectively the upper and lower limits of the electricity quantity allocation of thermal power enterprise g, C g is the installed capacity of thermal power enterprise g, F g is the failure rate of thermal power enterprise g, I g is the carbon emission intensity of thermal power enterprise g, δ and ∈ are adjustment coefficients determined according to the unit reliability and carbon emission situation respectively, α is a positive coefficient less than 1, representing the proportion considering unit maintenance and minimum stable operation requirements, S r is the system reserve demand, and β is the reserve demand allocation coefficient determined according to the system reliability standard;

[0021]

[0022] Among them: Among them: is the lower limit of the power allocation of wind power enterprise w, C w is the installed capacity of wind power enterprise w, γ is a positive coefficient related to the stability of local wind speed resources, η w is the average power generation efficiency of wind power enterprise w;

[0023] The power allocation range of the electricity-consuming enterprise is to Specifically as follows:

[0024]

[0025] Among them: Determined according to the basic production demand and historical minimum power consumption level of electricity-consuming enterprise j, is the historical average load of electricity-consuming enterprise j, is a positive coefficient less than 1;

[0026]

[0027] Among them: is to consider the load peak of electricity-consuming enterprise j and the demand preference for low-carbon electricity, D j is the set of low-carbon power generation enterprises, and θ is a positive coefficient adjusted according to the peak-valley difference.

[0028] Furthermore, adjusting the random power allocation range of the power generation enterprise or the random power allocation range of the electricity-consuming enterprise based on the unit reliability factor, carbon emission factor, and government authorization contract factor includes:

[0029] Adjustment formula based on the unit reliability factor:

[0030]

[0031] Among them: and are the upper limits of the power allocation of the power generation enterprise before and after adjustment respectively, λ is an adjustment coefficient, 0 < λ < 1, ΔF g is the change in the failure rate;

[0032] Adjustment formula based on the carbon emission factor:

[0033]

[0034] Among them: and are the power allocation amounts before and after adjustment respectively, ω is an inertia coefficient, 0 < ω < 1, and μ is a carbon emission intensity adjustment coefficient, 0 < μ < 1;

[0035]

[0036] Where: Q r is the rewarded electricity quantity, is the upper limit of electricity quantity allocation for electricity-consuming enterprises, ΔE j is the difference between the actual carbon emissions and the target carbon emissions of the electricity-consuming enterprise, v is the reward coefficient, 0 < v < 1;

[0037] Adjustment formula based on government-authorized contract factors:

[0038]

[0039] Where: Q p is the reduction in electricity quantity allocation, ΔQ c is the difference in unfulfilled contract electricity quantity, ξ is the default penalty coefficient, 0 < ξ < 1.

[0040] Furthermore, determining the stochastic clearing model according to the stochastic distribution model of electricity price and the range distribution model of electricity quantity allocation, and performing clearing calculation and optimization based on the stochastic clearing model, including:

[0041] Constructing an objective function with the profit of power generation enterprises and the utility of electricity-consuming enterprises as target parameters;

[0042] Determining constraint conditions according to the stochastic distribution model of electricity price and the range distribution model of electricity quantity allocation;

[0043] Determining the stochastic clearing model according to the objective function and constraint conditions.

[0044] Furthermore, constructing the objective function with the profit of power generation enterprises and the utility of electricity-consuming enterprises as target parameters, including:

[0045]

[0046] Where: I is the set of power generation enterprises, J is the set of electricity-consuming enterprises, π i (G i ,P i ) is the profit function of power generation enterprise i, P i is the electricity price of power generation enterprise i, G i is the power generation quantity of power generation enterprise i, C ij (L j ,Q j ) is the electricity purchase cost function of electricity-consuming enterprise j from power generation enterprise i, L j is the electricity purchase quantity of electricity-consuming enterprise j, Q j is the electricity purchase price of electricity-consuming enterprise j.

[0047] Furthermore, determining the constraint conditions according to the stochastic distribution model of electricity price and the range distribution model of electricity quantity allocation, including:

[0048] Power balance constraint:

[0049]

[0050] Where: K is the set of load forecasting error scenarios, and ΔL k is the load forecasting error under scenario k, and its upper and lower limits can be determined based on the fluctuation range of historical load data. S is the set of reserve resources, and R s is the output of reserve resource s, which is used to cope with load uncertainty;

[0051]

[0052] Where: ρ i and ω i are adjustment coefficients related to unit reliability and carbon emissions; if the unit of power generation enterprise i has high reliability and low carbon emissions, ρ i and ω i take smaller values; otherwise, they take larger values; is the minimum power generation considering the minimum stable operation of the unit and carbon emission requirements, is the installed capacity or the maximum power generation adjusted according to carbon emissions, etc.;

[0053] Transmission capacity constraint:

[0054]

[0055] Where: I l and J l are the sets of power generation enterprises and power consumption enterprises related to transmission line l, and K l is a subset of the line load forecasting error scenarios, is the maximum transmission capacity of transmission line l, and γ l is a coefficient related to the carbon emissions and reliability of the transmission line;

[0056] Unit ramp rate constraint:

[0057]

[0058]

[0059] Where: t represents time, G i (t) and G i (t−1) are the power generations of power generation enterprise i at times t and t−1 respectively, and are the minimum and maximum ramp rates under normal conditions of the unit, and K i is the set of unit i ramp rate uncertainty scenarios, and is the ramp rate deviation under scenario k;

[0060] Total carbon emission constraint:

[0061]

[0062] Where: E i = e i G i , e i is the carbon emission coefficient per unit of power generation of power generation enterprise i, and G i is the power generation of power generation enterprise i. E max is the maximum allowable carbon emission of the system, which is determined by the regional carbon emission target;

[0063] Low-carbon power ratio constraint:

[0064]

[0065] Where: I l is the set of low-carbon power generation enterprises, and p l is the minimum proportion of low-carbon power in the total power generation;

[0066] Contract power quantity constraint and contract price constraint:

[0067] G i ≥ Q ci

[0068] P i ≤ P ci

[0069] Where: for power generation enterprise i with a contract with the government, Q ci is the minimum power generation specified in the contract; when power generation enterprise i supplies the contract power quantity, its electricity price P i shall not exceed the contract price P ci .

[0070] Furthermore, determining the transaction result according to the clearing calculation and optimization, and performing electricity bill settlement according to the winning bid electricity price and actual electricity consumption in the transaction result, includes:

[0071] Determining transaction data according to the clearing calculation and optimization result, and performing electricity bill settlement calculation between power generation enterprises and electricity consumption enterprises based on the transaction data;

[0072] Executing settlement and recording through a preset system, and feeding back the result according to the established dispute handling mechanism.

[0073] The second aspect of the embodiments of the present application provides a power trading system based on a stochastic clearing mode, including:

[0074] A data acquisition and analysis unit, configured to acquire historical power supply and demand data, carbon emission data of a preset area, and real-time monitored supply and demand data, and perform power supply and demand analysis on the acquired data;

[0075] A random distribution model determination unit, configured to determine a random distribution model of the power price in the preset area based on the power supply and demand analysis result;

[0076] A range distribution model determination unit, configured to acquire power-related data of power generation enterprises and power consumption enterprises in the preset area, and determine a range distribution model of power quantity allocation according to the acquired data;

[0077] A random clearing model determination unit, configured to determine a random clearing model according to the random distribution model of the power price and the range distribution model of the power quantity allocation; and perform clearing calculation and optimization based on the random clearing model;

[0078] A power bill settlement unit, configured to determine a transaction result according to the clearing calculation and optimization, and perform power bill settlement according to the winning bid price and the actual power consumption quantity in the transaction result.

[0079] As can be seen from the above technical solutions, the embodiments of the present application have the following advantages:

[0080] By considering price randomness and power quantity randomness, the present invention can better reflect the uncertain factors of both supply and demand sides in the power market; determine the random fluctuation range and probability distribution model of the price according to the market supply and demand balance, historical price fluctuation range, and various cost change trends, and formulate the random range and adjustment rules of power quantity allocation based on various characteristics of power generation enterprises and power consumption enterprises, which helps to guide the flow of resources to a more efficient and reasonable direction; in the clearing calculation process, by satisfying multiple constraint conditions, the safe and stable operation of the power system is ensured; due to considering the randomness and uncertainty of various factors, all market participants face the same market environment and rules, avoiding unfair competition caused by information asymmetry or fixed rules; by establishing a strict verification mechanism and data management system, the accuracy of power bill settlement is ensured, and the legitimate rights and interests of market participants are protected. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 It is a schematic flowchart of an embodiment of a power trading method based on a random clearing mode in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0082] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0083] In this embodiment, a power trading method based on a random clearing mode is used to improve the fairness of power trading and the accuracy of power settlement. The implementation method in this embodiment can be implemented in the system, on the server, or on the terminal, and no specific limitation is made.

[0084] Embodiment 1

[0085] Please refer to Figure 1 , a power trading method based on a random clearing mode in the present invention includes the following steps:

[0086] S11. Obtain the historical power supply and demand data, carbon emission data of a preset region, and the supply and demand data monitored in real time, and perform power supply and demand analysis on the obtained data;

[0087] Obtaining the historical power supply and demand data, carbon emission data, and the supply and demand data monitored in real time includes collecting the power supply and demand data of the past few years from channels such as the Guangxi Power Trading Platform and the operation database of the power grid company. Among them, it is recorded in time series, including the total power generation of different power source types daily, monthly, and annually, as well as the total power consumption of the main power-consuming departments such as industry, commerce, and residents. Obtain the carbon emission reports of power generation enterprises in the Guangxi region, record the carbon emissions of each power generation enterprise in different time periods, and associate them with the corresponding power generation. In addition, collect the trading data of the carbon market in the Guangxi region, including data information such as carbon quota prices and carbon trading volumes, which affect the costs of power generation enterprises and power prices. Use the intelligent monitoring system of the Guangxi Power Grid to collect power supply and demand data in real time. On the power generation side, through the intelligent electricity meters and sensors installed at the outlet of the generator sets, collect data such as power generation power and unit operation status at regular intervals, and transmit them to the data center through the communication network. On the power consumption side, through the intelligent electricity meters and load monitoring systems, obtain the power consumption load data of various users in real time, and also collect and transmit them at regular time intervals.

[0088] Perform trend analysis on the obtained data for power supply and demand data, analyze power supply and demand, and the correlation between carbon emissions and other factors. Specifically, use the moving average method to calculate the long-term trends of power demand and supply. Taking power demand as an example, calculate the moving average power consumption in the past 12 months, draw a trend curve, and observe its growth or fluctuation trend. Analyze the changing trend of carbon emission data over time and its relationship with the adjustment of the power generation structure. For example, as the proportion of clean energy power generation such as hydropower and wind power in Guangxi increases, observe the changing trends of the total carbon emissions and carbon emission intensity. Through drawing charts such as line charts and bar charts, visually display the trend changes of the data. Analyze the correlation between power supply and demand, carbon emissions and other factors. Use the Pearson correlation coefficient to calculate the correlation between power demand and the economic growth indicators in the Guangxi region, as well as the correlation between carbon emissions and the power generation fuel structure. For example, if the calculated Pearson correlation coefficient between power demand and the growth rate of industrial added value is high, it indicates that industrial production has a significant impact on power demand; if the correlation between carbon emissions and the proportion of coal power generation is strong, it indicates that coal power generation is one of the main sources of carbon emissions.

[0089] S12. Determine the stochastic distribution model of the power price in the preset region based on the power supply and demand analysis results;

[0090] In this embodiment, step S12 includes the following:

[0091] 1. Determine the probability distribution model and parameters of the preset region according to the power supply and demand analysis results;

[0092] According to the data analysis results, determine the probability distribution model and parameters suitable for the power price in the Guangxi region. If the power price data shows a relatively symmetric distribution characteristic and there are no obvious extreme values or heavy tails, select the normal distribution to represent the probability distribution model. If the data has a right-skewed distribution and the price value range is in the positive interval, select the lognormal distribution to represent the probability distribution model. At the same time, use non-parametric test methods to test the goodness of fit between the data and different candidate distribution models. For example, perform the Kolmogorov-Smirnov test on the power price data in the Guangxi region in the past 5 years, compare its fitting effects with the normal distribution and the lognormal distribution, and select the model with the highest goodness of fit as the preliminary candidate model. After determining the probability model, perform model parameter estimation as follows:

[0093] For the selected probability distribution model, use the maximum likelihood method to estimate the model parameters. Taking the normal distribution as an example, let the power price data in the Guangxi region be X = {x 1 , x 2 , …, x n}, and the probability density function of the normal distribution is:

[0094]

[0095] where: μ is the mean, and σ 2 is the variance.

[0096] Using the maximum likelihood estimation method, the likelihood function is:

[0097]

[0098] By taking the derivative of the likelihood function and setting the derivative to 0, the estimated values of μ and σ 2 are obtained. For the lognormal distribution, its probability density function is:

[0099]

[0100] 2. Update the model parameters using the real-time monitored data to obtain an updated stochastic distribution model.

[0101] As the real-time monitored data is continuously updated, periodically re-evaluate the changes in power supply and demand and carbon emissions. If significant fluctuations in power demand are found, or major changes occur in the power generation structure, as well as carbon emission policy adjustments, etc., accordingly adjust the parameters of the probability distribution model. For example, if it is monitored in real time that the power demand continues to grow and is expected to continue for some time, it may be necessary to increase the mean and variance of the normal distribution model to reflect the increased likelihood of rising power prices.

[0102] The above steps can accurately analyze the historical power supply and demand data, carbon emission data, and real-time monitored supply and demand data in the Guangxi region, select an appropriate probability distribution model and estimate its parameters, and at the same time establish a dynamic adjustment mechanism to provide a data basis for determining the stochastic fluctuation range and probability distribution model of power prices in the Guangxi region.

[0103] S13. Obtain the power-related data of power generation enterprises and power consumption enterprises within a preset region, and determine the range distribution model of power allocation based on the obtained data;

[0104] In this embodiment, step S13 includes the following:

[0105] 1. Determine the random range of power allocation for power generation enterprises and the random range of power allocation for power consumption enterprises respectively;

[0106] 2. Adjust the random range of power allocation for power generation enterprises or the random range of power allocation for power consumption enterprises based on factors such as unit reliability, carbon emissions, and government authorized contracts.

[0107] Obtain power generation enterprise data and power consumption enterprise data: The power generation enterprise data includes the installed capacity distribution data, unit reliability data, carbon emission data, and government authorized contract related data of power generation enterprises in Guangxi region. The unit reliability includes index data such as the failure rate and mean time between failures of each power generation enterprise's units; the carbon emission data includes the historical carbon emission data and carbon emission intensity per unit of power generation of power generation enterprises; the government authorized contracts include energy security contracts, renewable energy development contracts, etc. The power consumption enterprise data includes load characteristics and carbon emission requirements, where the load characteristics are to collect the daily load curve of power consumption enterprises and analyze their peak-valley difference and load rate.

[0108] Determine the random range of power allocation based on the obtained data, that is, determine the upper and lower limits of power allocation for power generation enterprises. The specific expressions are as follows:

[0109] The random range expression of power allocation for power generation enterprises includes:

[0110]

[0111] Where: and are the upper and lower limits of power allocation for thermal power enterprise g respectively. C g is the installed capacity of thermal power enterprise g, F g is the failure rate of thermal power enterprise g, I g is the carbon emission intensity of thermal power enterprise g. δ and ∈ are adjustment coefficients determined according to unit reliability and carbon emission conditions respectively. α is a positive coefficient less than 1, representing the proportion considering unit maintenance and minimum stable operation requirements. S r is the system reserve demand, and β is the reserve demand allocation coefficient determined according to the system reliability standard;

[0112]

[0113] Where: is the lower limit of power allocation for wind power enterprise w. C w is the installed capacity of wind power enterprise w, and γ is a positive coefficient related to the stability of local wind speed resources. η w is the average power generation efficiency of wind power enterprise w;

[0114] The power allocation range for power consumption enterprises is to Specifically as follows:

[0115]

[0116] Where: is determined according to the basic production demand and historical minimum power consumption level of power consumption enterprise j. is the historical average load of power consumption enterprise j. is a positive coefficient less than 1.

[0117]

[0118] Where: is to consider the peak load of electricity-consuming enterprise j and its demand preference for low-carbon electricity. D j is the set of low-carbon power generation enterprises, and θ is a positive coefficient adjusted according to the peak-valley difference. If the proportion of low-carbon electricity demand of electricity-consuming enterprises is high, its upper limit will increase accordingly to encourage the consumption of low-carbon electricity.

[0119] Adjustment based on unit reliability: When the unit reliability index of power generation enterprises, such as the failure rate F g changes, recalculate its power distribution range. If the failure rate increases, reduce its upper limit of power distribution The adjustment formula is:

[0120]

[0121] Where: λ is an adjustment coefficient, 0 < λ < 1, ΔF g is the change in the failure rate.

[0122] For units with high reliability, their power distribution priority can be appropriately increased, and certain preferential policies can be given in the market transaction, such as reducing transaction fees or giving priority to participating in power distribution.

[0123] Adjustment based on carbon emissions:

[0124] Sort according to the carbon emission intensity I of power generation enterprises g For enterprises with low carbon emission intensity, increase their power distribution ratio. The adjustment formula is:

[0125]

[0126] Where: and are the power distribution amounts before and after adjustment respectively. ω is an inertia coefficient, 0 < ω < 1, and μ is a carbon emission intensity adjustment coefficient, 0 < μ < 1.

[0127] If an electricity-consuming enterprise exceeds the carbon emission reduction target, a certain amount of electricity reward can be given based on its upper limit of power distribution The calculation formula for the rewarded electricity is:

[0128]

[0129] Where: ΔE j is the difference between the actual carbon emissions and the target carbon emissions of the electricity-consuming enterprise, and v is a reward coefficient, 0 < v < 1.

[0130] Adjustment based on government - authorized contracts:

[0131] Regularly check the performance of government - authorized contracts. If the power generation enterprise fails to supply electricity according to the contract provisions, handle it according to the default terms, such as deducting a certain percentage of the performance bond and reducing its subsequent electricity allocation volume. The reduction amount calculation formula is:

[0132]

[0133] Where: ΔQ c is the difference in the contract electricity volume not fulfilled, ξ is the default penalty coefficient, and 0 < ξ < 1.

[0134] S14. Determine the stochastic clearing model based on the stochastic distribution model of electricity prices and the range distribution model of electricity allocation, and perform clearing calculations and optimizations based on the stochastic clearing model;

[0135] 1. Construct an objective function with the profit of power generation enterprises and the utility of electricity - using enterprises as target parameters;

[0136] Extract the stochastic fluctuation interval and probability distribution model of prices from the models constructed above. For example, if it is determined that the price follows a normal distribution, then during the clearing calculation, a series of possible price samples are randomly generated according to this distribution. For the stochastic range and adjustment rules of electricity allocation, according to the established formulas and parameters, combined with the actual situations of power generation enterprises and electricity - using enterprises, determine the possible electricity allocation ranges of each enterprise under different scenarios. For example, a thermal power generation enterprise determines possible electricity allocation values within the lower and upper limits of electricity allocation according to its installed capacity, unit reliability, and carbon emission situation.

[0137] Set the objective function and constraint conditions based on the robust optimization algorithm for uncertainty. Through optimization calculations, determine the winning electricity volume and winning electricity price of each power generation enterprise. At the same time, determine the electricity purchase volume and electricity purchase price of each electricity - using enterprise to meet its load demand. Specifically as follows:

[0138] Objective function:

[0139]

[0140] Where: I is the set of power generation enterprises, J is the set of electricity - using enterprises, π i (G i ,P i ) is the profit function of power generation enterprise i, P i is the electricity price of power generation enterprise i, G i is the electricity generation volume of power generation enterprise i, C ij (L j ,Q j) is the cost function for electricity-consuming enterprise j to purchase electricity from power generation enterprise i, L j is the electricity purchase quantity of electricity-consuming enterprise j, Q j is the electricity purchase price of electricity-consuming enterprise j.

[0141] 2. Determine the constraint conditions according to the stochastic distribution model of electricity price and the range distribution model of electricity quantity allocation;

[0142] The constraint conditions include power balance constraint, generation capacity constraint, transmission capacity constraint, unit ramp rate constraint, total carbon emission constraint, low-carbon electricity proportion constraint, contract electricity quantity constraint and contract price constraint;

[0143] Power balance constraint:

[0144]

[0145] Among them: K is the set of load forecasting error scenarios, ΔL k is the load forecasting error under scenario k, and its upper and lower limits can be determined based on the fluctuation range of historical load data. S is the set of reserve resources, R s is the output of reserve resource s, which is used to cope with load uncertainty.

[0146] Generation capacity constraint:

[0147]

[0148] Among them: ρ i and ω i are adjustment coefficients related to unit reliability and carbon emission. If the unit of power generation enterprise i has high reliability and low carbon emission, ρ i and ω i take smaller values; otherwise, they are larger; is the minimum power generation considering the minimum stable operation of the unit and carbon emission requirements, is the installed capacity or the maximum power generation adjusted according to carbon emission, etc. For example, if power generation enterprise i is a thermal power enterprise with a relatively high carbon emission intensity and aging units, may be relatively high due to the need to maintain a certain operating efficiency and meet the minimum carbon emission requirements, may be lower than the installed capacity due to unit reliability problems.

[0149] Transmission capacity constraint:

[0150]

[0151] Among them: I l and J l are the sets of power generation enterprises and electricity-consuming enterprises related to transmission line l, K l is the subset of line load forecasting error scenarios, is the maximum transmission capacity of the transmission line l, and γ l is a coefficient related to the carbon emissions and reliability of the transmission line. If the transmission line l is aging and has a high carbon emissions, γ l takes a larger value to limit its transmission capacity; otherwise, it takes a smaller value.

[0152] Unit ramp rate constraint:

[0153]

[0154] where: t represents time, and G i (t) and G i (t−1) are the power generation amounts of power generation enterprise i at times t and t−1 respectively, and are the minimum and maximum ramp rates under normal conditions of the unit, and K i is the set of scenarios of the ramp rate uncertainty of unit i, and are the ramp rate deviations under scenario k.

[0155] Total carbon emissions constraint:

[0156]

[0157] where: E i =e i G i , e i is the carbon emissions coefficient per unit power generation of power generation enterprise i, G i is the power generation amount of power generation enterprise i, and E max is the maximum allowable carbon emissions of the system, which is determined by the regional carbon emissions target.

[0158] Low-carbon power ratio constraint:

[0159]

[0160] where: I l is the set of low-carbon power generation enterprises, and p l is the minimum proportion of low-carbon power in the total power generation amount specified.

[0161] Contract power quantity constraint and contract price constraint:

[0162] G i ≥Q ci

[0163] P i ≤P ci

[0164] where: for power generation enterprise i with a contract with the government, Q ciis the minimum power generation specified in the contract; when power generation enterprise i supplies the contract power, its electricity price P i shall not exceed the contract price P ci .

[0165] 3. Determine the stochastic clearing model according to the objective function and constraint conditions.

[0166] The scenario-based robust optimization algorithm will generate multiple scenarios for load forecasting errors, unit ramp rate uncertainties, etc., solve the optimization problem under each scenario, and determine the final solution through certain robustness criteria. Continuously adjust the power generation volume G i of power generation enterprises, electricity price P i and the power purchase volume L j of power consumption enterprises, as well as the power purchase electricity price Q j to find the solution that satisfies the constraint conditions and optimizes the objective function.

[0167] In each iteration process, check the constraint conditions for the generated solution: check the power balance constraint, if not satisfied, discard the solution; check the power generation capacity constraint, if G i is not within the specified range, exclude the solution; check the transmission capacity constraint, and do not consider the solution that violates this constraint; check the unit ramp constraint, and discard the solution that is not satisfied; check the electricity-carbon traceability constraint and the government-authorized contract constraint, and eliminate the solution that does not meet the requirements.

[0168] After multiple iterations and screenings, obtain the optimal solution that satisfies all constraint conditions. At this time:[[]]

[0169] The winning bid volume of power generation enterprise i is G i in the final solution, and the winning bid electricity price is P i ; the power purchase volume of power consumption enterprise j is L j and the power purchase electricity price is Q j .

[0170] S15. Determine the transaction result according to the clearing calculation and optimization, and conduct electricity bill settlement according to the winning bid electricity price and actual electricity consumption in the transaction result.

[0171] In this embodiment, step S15 includes the following:[[]]

[0172] 1. Determine the transaction data according to the clearing calculation and optimization results, and conduct the electricity bill settlement calculation between power generation enterprises and power consumption enterprises based on the transaction data;

[0173] Build a power trading result database to store the key information after clearing calculation and optimization. The power generation enterprises include fields such as enterprise name, winning bid electricity quantity, winning bid electricity price, and trading timestamp. For example, a thermal power enterprise A won a bid for 500 MWh of electricity quantity at a winning bid electricity price of 350 yuan / MWh in a certain transaction, and the trading time was 10:00 on October 1, 2024. The power consumption enterprises include information such as enterprise name, purchased electricity quantity, purchased electricity price, and trading timestamp, etc. Calculate the electricity bills of power consumption enterprises and the electricity revenue of power generation enterprises respectively. Obtain the actual power generation and power consumption data from the metering devices of power generation enterprises and power consumption enterprises. Compare these actual data with the winning bid electricity quantity and purchased electricity quantity in the transaction records. Reconfirm whether the winning bid electricity price and purchased electricity price in the transaction records comply with the market rules and clearing results.

[0174] 2. Execute settlement and record through a preset system, and feedback the results according to the established dispute handling mechanism.

[0175] If the power generation enterprise is a renewable energy power generation enterprise such as wind power or photovoltaic, determine the subsidy amount according to the renewable energy subsidy policy of the country or region. For power consumption enterprises, adjust the electricity bills according to the peak-valley electricity price policy based on whether their electricity consumption time is in the peak-valley period. Set up a special dispute handling department or position to be responsible for receiving and handling disputes and abnormal situation reports raised by power generation enterprises and power consumption enterprises during the electricity bill settlement process. For abnormal situations, such as partial loss of transaction data due to data transmission interruption or sudden failure of metering devices affecting the accuracy of electricity quantity measurement, etc., emergency plans should be formulated.

[0176] After completing all the above steps, including transaction result recording, electricity bill calculation, information verification, price adjustment, and dispute handling, complete the electricity bill settlement between power generation enterprises and power consumption enterprises according to the finally determined electricity bill amount.

[0177] The above embodiments can standardize the electricity bill settlement process in the power market, protect the legitimate rights and interests of power generation enterprises and power consumption enterprises, and promote the stable, fair, and orderly operation of the power market. In actual operation, the technical means and management measures of each link should be continuously improved to improve the efficiency and accuracy of electricity bill settlement.

[0178] Embodiment 2

[0179] An embodiment of a power trading system based on a stochastic clearing mode in the present invention includes the following steps:

[0180] A data acquisition and analysis unit for acquiring historical power supply and demand data, carbon emission data, and real-time monitored supply and demand data of a preset region, and performing power supply and demand analysis on the acquired data;

[0181] A stochastic distribution model determination unit for determining a stochastic distribution model of the power price in a preset region based on the power supply and demand analysis results;

[0182] A range distribution model determination unit, configured to obtain power-related data of power generation enterprises and power consumption enterprises within a preset area, and determine a range distribution model for power quantity allocation according to the obtained data;

[0183] A stochastic clearing model determination unit, configured to determine a stochastic clearing model according to a stochastic distribution model of electricity prices and a range distribution model of power quantity allocation, and perform clearing calculation and optimization based on the stochastic clearing model;

[0184] An electricity fee settlement unit, configured to determine a transaction result according to the clearing calculation and optimization, and perform electricity fee settlement according to the winning bid price and the actual electricity consumption amount in the transaction result.

[0185] Those of ordinary skill in the art can realize that the units of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components of each example have been generally described according to their functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0186] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc. In addition, the functional units in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software function units.

[0187] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0188] It can be understood that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present invention, and they should all be covered within the scope of the claims and the description of the present invention.

Claims

1. A power trading method based on a random clearing model, characterized in that: include: Obtain historical power supply and demand data, carbon emission data and real-time monitored supply and demand data for a preset area, and use the acquired data to conduct power supply and demand analysis; Determine a random distribution model of electricity prices in the preset area based on the power supply and demand analysis results; Obtaining power-related data of power generation enterprises and power consumption enterprises in the preset area, and determining a range distribution model of power distribution based on the acquired data; Determining a random clearing model according to the random distribution model of the electricity price and the range distribution model of the electricity distribution, and performing clearing calculation and optimization based on the random clearing model; The transaction results are determined based on clearing calculations and optimization, and the electricity charges are settled according to the winning electricity price in the transaction results and the actual electricity consumption.

2. The power trading method based on the random clearing mode according to claim 1 is characterized in that: The random distribution model for determining the electricity price in the preset area based on the power supply and demand analysis result includes: Determine the probability distribution model and parameters of the preset area according to the power supply and demand analysis results; The model parameters are updated using the real-time monitored data to obtain an updated random distribution model.

3. The power trading method based on the random clearing mode according to claim 1 is characterized in that: The obtaining of power-related data of power generation enterprises and power consumption enterprises in the preset area, and determining a range distribution model of power distribution according to the obtained data, includes: Determine the random range of electricity allocation for power generation enterprises and the random range of electricity allocation for power consumption enterprises respectively; The random range of electricity distribution of the power generation enterprise or the random range of electricity distribution of the power consumption enterprise is adjusted based on the unit reliability factor, the carbon emission factor and the government authorized contract factor.

4. The power trading method based on the random clearing mode according to claim 3 is characterized in that: The step of respectively determining the random range of electricity distribution of the power generation enterprise and the random range of electricity distribution of the power consumption enterprise comprises: The random range expressions of power distribution of power generation enterprises include: in: and are the upper and lower limits of power allocation for thermal power companies g, C g is the installed capacity of thermal power company g, F g is the failure rate of thermal power company g, I g is the carbon emission intensity of thermal power company g, δ and ∈ are adjustment coefficients determined according to unit reliability and carbon emission conditions, respectively, α is a positive coefficient less than 1, indicating the proportion of unit maintenance and minimum stable operation requirements, S r is the system reserve demand, β is the reserve demand allocation coefficient determined according to the system reliability standard; in: is the lower limit of the electricity allocation of wind power enterprise w, C w is the installed capacity of wind power company w, γ is a positive coefficient related to the stability of local wind speed resources, η w is the average power generation efficiency of wind power enterprise w; The power distribution range of power consuming enterprises is arrive The details are as follows: in: Determined based on the basic production demand and historical lowest electricity consumption level of electricity-consuming enterprise j, is the historical average load of electricity consumer j, is a positive coefficient less than 1; in: To consider the load peak and demand preference for low-carbon electricity of electricity user j, D j is a set of low-carbon power generation enterprises, and θ is a positive coefficient adjusted according to the peak-valley difference.

5. The power trading method based on random clearing mode according to claim 3 is characterized in that: The adjusting of the random range of electricity distribution of the power generation enterprise or the random range of electricity distribution of the power consumption enterprise based on the unit reliability factor, the carbon emission factor and the government authorized contract factor includes: Adjustment formula based on unit reliability factor: in: and are the upper limits of power generation enterprise electricity allocation before and after adjustment, λ is an adjustment coefficient, 0<λ<1, ΔF g is the change in failure rate; Adjustment formula based on carbon emission factors: in: and are the electricity distribution before and after adjustment, ω is an inertia coefficient, 0<ω<1, μ is the carbon emission intensity adjustment coefficient, 0<μ<1; Where: Q r To reward electricity, is the upper limit of electricity consumption for electricity users, ΔE j is the difference between the actual carbon emissions of the electricity user and the target carbon emissions, v is the reward coefficient, and 0 <v<1; Adjustment formula based on government-mandated contract factors: Where: Q p is the reduction in power allocation, ΔQ c is the difference in the unfulfilled contractual electricity volume, ξ is the penalty coefficient for breach of contract, 0<ξ<1.

6. The power trading method based on random clearing mode according to claim 1 is characterized in that: The method of determining a random clearing model according to the random distribution model of electricity prices and the range distribution model of electricity distribution, and performing clearing calculation and optimization based on the random clearing model includes: The objective function is constructed with the profit of power generation enterprises and the utility of power users as target parameters; Determining constraint conditions according to the random distribution model of the electricity price and the range distribution model of the electricity distribution; The stochastic clearing model is determined according to the objective function and the constraints.

7. The power trading method based on random clearing mode according to claim 6 is characterized in that: The objective function is constructed by taking the profit of the power generation enterprise and the utility of the power consumption enterprise as the target parameters, including: Where: I is the set of power generation enterprises, J is the set of power consumption enterprises, π i (G i ,P i ) is the profit function of power generation enterprise i, P i is the electricity price of power generation company i, G i is the power generation of power generation company i, C ij (L j ,Q j ) is the cost function for electricity user j to purchase electricity from power generation enterprise i, L j is the amount of electricity purchased by electricity user enterprise j, Q j is the electricity purchase price of electricity user j.

8. The power trading method based on the random clearing mode according to claim 6 is characterized in that: The determining of constraint conditions according to the random distribution model of the electricity price and the range distribution model of the electricity distribution includes: Power balance constraints: Where: K is the load forecast error scenario set, ΔL k is the load forecast error under scenario k, and its upper and lower limits are determined based on the fluctuation range of historical load data. S is the set of backup resources, and R s is the output of the reserve resource s, which is used to cope with load uncertainty; Where: i and ω i is the adjustment coefficient related to unit reliability and carbon emissions; if the reliability of power generation company i is high and the carbon emissions are low, ρ i and ω i The value is smaller; otherwise, it is larger; It is the minimum power generation considering the minimum stable operation and carbon emission requirements of the unit. It is the installed capacity or the maximum electricity generation after adjustment for carbon emissions, etc.; Transmission capacity constraints: Where: I l and J l is the set of power generation enterprises and power consumption enterprises related to the transmission line l, K l is a subset of line load forecast error scenarios, is the maximum transmission capacity of transmission line l, γ l is the coefficient related to carbon emissions and reliability of transmission lines; Unit climbing constraints: Among them: t represents time, G i (t) and G i (t-1) is the power generation of power generation enterprise i at time t and t-1, and is the minimum and maximum ramp rate of the unit under normal conditions, K i is the set of uncertain scenarios of ramp rate of unit i, and is the climbing rate deviation under scenario k; Total carbon emission constraints: Where: E i =e i G i , e i is the carbon emission coefficient per unit of electricity generated by power generation enterprise i, G i is the power generation of power generation enterprise i, E max is the maximum carbon emission allowed by the system, determined by the regional carbon emission target; Low-carbon electricity ratio constraints: Where: I l For low-carbon power generation companies, p l It is a prescribed minimum proportion of low-carbon electricity in total electricity generation; Contract quantity constraints and contract price constraints: G i ≥Q ci P i ≤P ci Where: For power generation company i that has a contract with the government, Q ci is the minimum power generation specified in the contract; when power generation company i supplies the contract power, its electricity price P i Cannot exceed the contract price P ci .

9. The power trading method based on random clearing mode according to claim 1, characterized in that: The transaction result is determined based on the clearing calculation and optimization, and the electricity fee is settled according to the winning bid electricity price and actual electricity consumption in the transaction result, including: Determine transaction data based on clearing calculation and optimization results, and calculate electricity bills between power generation enterprises and power users based on transaction data; Settlements are executed and recorded through a preset system, and the results are fed back based on the established dispute resolution mechanism.

10. An electricity trading system based on a random clearing model, characterized in that: include: A data acquisition and analysis unit, used to acquire historical power supply and demand data, carbon emission data and real-time monitored supply and demand data of a preset area, and to perform power supply and demand analysis on the acquired data; A random distribution model determination unit, used to determine the random distribution model of the electricity price in the preset area based on the power supply and demand analysis result; A range distribution model determination unit, used to obtain power-related data of power generation enterprises and power consumption enterprises in the preset area, and determine a range distribution model for power distribution according to the obtained data; A random clearing model determination unit, configured to determine a random clearing model according to the random distribution model of the electricity price and the range distribution model of the electricity distribution; and to perform clearing calculation and optimization based on the random clearing model; The electricity fee settlement unit is used to determine the transaction results based on clearing calculations and optimization, and to settle electricity fees according to the winning bid electricity price and actual electricity consumption in the transaction results.

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