Electric quantity dispatching method and system for medium and long-term electric power market and spot market

Abstract

The invention discloses a power scheduling method and system for a power medium-and-long-term market and a spot market, and relates to the technical field of power markets, and the method comprises the steps: receiving initial medium-and-long-term contract power, decomposing the initial medium-and-long-term contract power through a medium-and-long-term curve decomposition mechanism, and obtaining medium-and-long-term contract decomposition power; obtaining price data of the medium-and-long-term market and price data of the spot market, inputting the price data of the medium-and-long-term market and the price data of the spot market into a pre-established vector autoregression (VAR) model, and outputting a prediction result of a dynamic relationship between the price of the medium-and-long-term market and the price of the spot market; inputting the price data of the medium and long term market and the price data of the spot market into a pre-established generalized autoregressive conditional heterovariance GARCH model, and outputting to obtain conditional volatility of the price; according to the prediction result of the dynamic relationship between the medium-and-long-term market price and the spot market price and the fluctuation of the price, the instant supply and demand condition of the spot market is determined, the instant supply and demand condition of the spot market is combined with the medium-and-long-term contract decomposition electric quantity, and coordinated scheduling of the market electric quantity is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power market, and in particular to a method and system for dispatching electric power in medium- and long-term power market and spot market. Background Art

[0002] In the spot market environment, medium- and long-term contracts need to be decomposed into curves, that is, the medium- and long-term transaction electricity volume is decomposed into the time scale of the spot market to achieve an organic connection with the spot market. In the medium- and long-term market, the electricity demand curve for a period of time in the future is formed through demand forecasting and contract signing. However, actual demand often changes due to various factors. Therefore, it is necessary to dynamically decompose and dispatch demand to ensure the balance between supply and demand. In addition, most of the current medium- and long-term electricity contracts are financial in nature and do not require physical execution. On this basis, when the spot price is low, power generators will choose to generate less or no electricity. When the spot market needs sufficient electricity to dispatch, there will be a shortage of electricity, which will lead to hidden dangers in the physical safety of the power system. Summary of the invention

[0003] In order to solve the deficiencies mentioned in the above background technology, the purpose of the present invention is to provide a method and system for dispatching electricity in the medium- and long-term electricity market and the spot market, so as to achieve a benign interaction between the medium- and long-term market and the spot market and ensure the long-term safe supply of electricity in the power system.

[0004] In the first aspect, the purpose of the present invention can be achieved by the following technical solution: a method for dispatching electricity in the medium- and long-term electricity market and the spot market, the method comprising the following steps:

[0005] Receive the initial medium- and long-term contract electricity, decompose the initial medium- and long-term contract electricity through the medium- and long-term curve decomposition mechanism, and obtain the medium- and long-term contract decomposed electricity;

[0006] Obtaining price data of the medium- and long-term market and price data of the spot market, inputting the price data of the medium- and long-term market and price data of the spot market into a pre-established vector autoregression VAR model, and outputting a prediction result of the dynamic relationship between the prices of the medium- and long-term market and the spot market;

[0007] Input the price data of the medium and long-term market and the price data of the spot market into the pre-established generalized autoregressive conditional heteroskedastic GARCH model, and output the conditional volatility of the price;

[0008] Based on the forecast results of the dynamic relationship between the medium- and long-term market and the spot market prices and the volatility of prices, the instant supply and demand situation of the spot market is determined, and the instant supply and demand situation of the spot market is combined with the medium- and long-term contracts to decompose the electricity volume, so as to achieve coordinated scheduling of market electricity volume.

[0009] In combination with the first aspect, in some implementations of the first aspect, the method further includes: decomposing the initial medium- and long-term contract electricity by using the medium- and long-term curve decomposition mechanism includes:

[0010] Collect historical electricity consumption data and power generation capacity data to identify peak and trough electricity consumption;

[0011] Determine the benchmark supply curve. The decomposition principles include smoothing the power supply curve, allocating the power supply in each period, and reserving adjustment space. The decomposition of power in each period is performed using the following formula:

[0012] Q i =α i Q c

[0013] Where: Q i Decompose the power consumption for each time period; α i Q is the power decomposition ratio; c The total contracted electricity volume is determined by the electricity volume decomposition ratio for each period.

[0014] In combination with the first aspect, in some implementations of the first aspect, the method further includes: using an optimization algorithm to optimize the allocation of the supply of medium- and long-term contracts through a dynamic adjustment mechanism: using a rolling optimization method, taking time nodes as units, setting k as the current calculation time node, and the objective function is to minimize the deviation between the supply and demand:

[0015]

[0016] Where: S k ′ is the supply after adjustment at time node k; D k ′ is the actual demand at time node k; T is the number of time cycle nodes for rolling optimization;

[0017] The constraints are:

[0018]

[0019] Where: S is the total supply; The minimum supply for each period.

[0020] In conjunction with the first aspect, in certain implementations of the first aspect, the method further includes: after the medium- and long-term market signs the medium- and long-term contract, implementing a supporting physical security mechanism, including:

[0021] Reliability contracts that oblige generators to provide physical power delivery under pre-set conditions and subject them to fines or other penalties if they fail to meet the contract;

[0022] Establish backup power generation mechanisms, including fast-start gas turbines or energy storage systems;

[0023] Establish and improve a balancing market mechanism to ensure that system operators can dispatch resources in real time to maintain system balance and stability. The balancing market dispatches power generation through price signals to ensure real-time supply and demand balance.

[0024] In combination with the first aspect, in certain implementations of the first aspect, the method further includes: preprocessing the price data of the medium and long-term markets and the price data of the spot market before inputting into the model, including cleaning and standardizing the data.

[0025] In combination with the first aspect, in certain implementations of the first aspect, the method further includes: the calculation formula for inputting the price data of the medium- and long-term markets and the price data of the spot market into the pre-established vector autoregression VAR model includes:

[0026]

[0027] Where: y t is a vector containing multiple variables, including medium- and long-term market prices and spot market prices; c is a constant vector; A i is the coefficient matrix of lag period i; u t is the error term vector.

[0028] In combination with the first aspect, in certain implementations of the first aspect, the method further includes: the calculation process of inputting the price data of the medium- and long-term markets and the price data of the spot market into a pre-established generalized autoregressive conditional heteroskedasticity GARCH model includes:

[0029]

[0030] Where: is the conditional volatility at time t; ω, α and β are the model parameters to be estimated; ∈ t-1 is the residual at time t-1.

[0031] In combination with the first aspect, in some implementations of the first aspect, the method further includes: the process of implementing coordinated dispatch of market electricity:

[0032] Determine the signing ratio of medium- and long-term contracts based on market demand and supply; regularly monitor market operation and adjust the signing ratio based on actual conditions;

[0033] Through price linkage, the prices of medium- and long-term contracts reflect the real-time supply and demand conditions of the spot market, including data collection, model building, price adjustment and monitoring feedback;

[0034] Perform initial power decomposition according to the power decomposition ratio, and introduce a dynamic adjustment mechanism to adjust the supply according to real-time demand changes;

[0035] By establishing a capacity market, signing reliability contracts, setting up a backup power generation mechanism and implementing demand response, we can ensure that power generation companies can provide electricity in the spot market and achieve coordinated dispatch of market electricity.

[0036] In a second aspect, in order to achieve the above-mentioned purpose, the present invention discloses an electric power dispatching system for the medium- and long-term electric power market and the spot market, comprising:

[0037] The power decomposition module is used to receive the initial medium- and long-term contract power, decompose the initial medium- and long-term contract power through the medium- and long-term curve decomposition mechanism, and obtain the medium- and long-term contract decomposition power;

[0038] The price prediction module is used to obtain the price data of the medium- and long-term market and the price data of the spot market, input the price data of the medium- and long-term market and the price data of the spot market into a pre-established vector autoregression VAR model, and output the prediction result of the dynamic relationship between the medium- and long-term market and the spot market prices;

[0039] The conditional volatility module is used to input the price data of the medium and long-term markets and the price data of the spot market into the pre-established generalized autoregressive conditional heteroskedastic GARCH model, and output the conditional volatility of the price;

[0040] The electricity dispatch module is used to determine the instant supply and demand situation of the spot market based on the forecast results of the dynamic relationship between the medium- and long-term market and the spot market prices and the volatility of prices, and to decompose the electricity volume by combining the instant supply and demand situation of the spot market with the medium- and long-term contracts to achieve coordinated dispatch of market electricity.

[0041] In conjunction with the second aspect, in some implementations of the second aspect, the system further includes: the power decomposition module decomposing the initial medium- and long-term contract power through the medium- and long-term curve decomposition mechanism includes:

[0042] Collect historical electricity consumption data and power generation capacity data to identify peak and trough electricity consumption;

[0043] Determine the benchmark supply curve. The decomposition principles include smoothing the power supply curve, allocating the power supply in each period, and reserving adjustment space. The decomposition of power in each period is performed using the following formula:

[0044] Q i =α i Q c

[0045] Where: Q i Decompose the power consumption for each time period; α i Q is the power decomposition ratio;c The total contracted electricity quantity is determined by the electricity quantity decomposition ratio for each period;

[0046] The power decomposition module uses an optimization algorithm to optimize the supply allocation of medium- and long-term contracts through a dynamic adjustment mechanism: a rolling optimization method is used, with time nodes as units, k is set as the current calculation time node, and the objective function is to minimize the deviation between supply and demand:

[0047]

[0048] Where: S k ′ is the supply after adjustment at time node k; D k ′ is the actual demand at time node k; T is the number of time cycle nodes for rolling optimization;

[0049] The constraints are:

[0050]

[0051] Where: S is the total supply; The minimum supply for each period;

[0052] After the mid- to long-term contract is signed in the price prediction module mid- to long-term market, a supporting physical security mechanism will be implemented, including:

[0053] Reliability contracts that oblige generators to provide physical power delivery under pre-set conditions and subject them to fines or other penalties if they fail to meet the contract;

[0054] Establish backup power generation mechanisms, including fast-start gas turbines or energy storage systems;

[0055] Establish and improve a balancing market mechanism to ensure that system operators can dispatch resources in real time to maintain system balance and stability, where the balancing market dispatches power generation through price signals to ensure real-time supply and demand balance;

[0056] The price data of the medium- and long-term markets and the price data of the spot market in the price prediction module are preprocessed before being input into the model, including cleaning and standardizing the data;

[0057] The price prediction module inputs the price data of the medium and long-term market and the price data of the spot market into the pre-established vector autoregression VAR model. The calculation formula includes:

[0058]

[0059] Where: y t is a vector containing multiple variables, including medium- and long-term market prices and spot market prices; c is a constant vector; Ai is the coefficient matrix of lag period i; u t is the error term vector;

[0060] The conditional volatility module inputs the price data of the medium and long-term market and the price data of the spot market into the pre-established generalized autoregressive conditional heteroskedasticity GARCH model. The calculation process includes:

[0061]

[0062] Where: is the conditional volatility at time t; ω, α and β are the model parameters to be estimated; ∈ t-1 is the residual at time t-1;

[0063] The power dispatch module realizes the process of coordinated dispatch of market power:

[0064] Determine the signing ratio of medium- and long-term contracts based on market demand and supply; regularly monitor market operation and adjust the signing ratio based on actual conditions;

[0065] Through price linkage, the prices of medium- and long-term contracts reflect the real-time supply and demand conditions of the spot market, including data collection, model building, price adjustment and monitoring feedback;

[0066] Perform initial power decomposition according to the power decomposition ratio, and introduce a dynamic adjustment mechanism to adjust the supply according to real-time demand changes;

[0067] By establishing a capacity market, signing reliability contracts, setting up a backup power generation mechanism and implementing demand response, we can ensure that power generation companies can provide electricity in the spot market and achieve coordinated dispatch of market electricity.

[0068] Beneficial effects of the present invention:

[0069] The present invention aims to achieve a benign interaction between the medium- and long-term market and the spot market and ensure the long-term safe supply of electricity in the power system by proposing a connection mechanism and a coordination plan between the medium- and long-term market and the spot market. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0071] Figure 1 It is a schematic flow chart of the method of the present invention;

[0072] Figure 2 It is a schematic diagram of the overall framework of the present invention;

[0073] Figure 3 It is a schematic diagram of the system structure of the present invention;

[0074] Figure 4 is a diagram showing the decomposition results of the power consumption in the present embodiment when the demand changes;

[0075] Figure 5 It is a schematic diagram of the historical data of medium and long-term contract prices and spot prices in this embodiment. DETAILED DESCRIPTION

[0076] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0077] Embodiment 1:

[0078] The following is an introduction to the relevant terms involved in the embodiments of the present application:

[0079] Power system: A unified whole consisting of power generation, power supply (transmission, transformation, distribution), power consumption facilities, and secondary facilities such as regulation and control, relay protection and safety automatic devices, metering devices, dispatching automation, power communications, etc. required to ensure their normal operation.

[0080] The power system is an electric energy production and consumption system consisting of power plants, power transmission and transformation lines, power distribution stations and power consumption. Its function is to convert primary energy in nature into electric energy through power generation devices, and then supply electric energy to various users through transmission, transformation and distribution. To achieve this function, the power system also has corresponding information and control systems at various links and levels to measure, regulate, control, protect, communicate and dispatch the production process of electric energy to ensure that users obtain safe and high-quality electric energy.

[0081] The main structure of the power system includes power sources (power plants such as hydropower stations, thermal power plants, nuclear power plants, etc.), substations (boosting substations, load center substations, etc.), transmission and distribution lines, and load centers. The power supply points are also interconnected to achieve power exchange and regulation between different regions, thereby improving the safety and economy of power supply. The network composed of transmission lines and substations is usually called a power network. The information and control system of the power system consists of various detection equipment, communication equipment, safety protection devices, automatic control devices, and monitoring automation and dispatching automation systems. The structure of the power system should ensure the reasonable coordination of power production and consumption on the basis of advanced technical equipment and high economic benefits.

[0082] like Figure 1 As shown, a method for dispatching electricity in the medium- and long-term electricity market and the spot market includes the following steps:

[0083] S101: receiving the initial medium- and long-term contract electricity, and decomposing the initial medium- and long-term contract electricity through the medium- and long-term curve decomposition mechanism to obtain the medium- and long-term contract decomposed electricity;

[0084] The initial medium- and long-term contract electricity is decomposed through the medium- and long-term curve decomposition mechanism, including:

[0085] Collect historical electricity consumption data and power generation capacity data to determine peak and trough electricity consumption; determine peak and trough electricity consumption, and make forecasts on short-term and long-term electricity demand in combination with future economic growth, power planning and policy changes.

[0086] Determine the benchmark supply curve. The decomposition principles include smoothing the power supply curve, allocating the power supply in each period, and reserving adjustment space. The decomposition of power in each period is performed using the following formula:

[0087] Q i =α i Q c

[0088] Where: Q i Decompose the power consumption for each time period; α i Q is the power decomposition ratio; c The total contracted electricity quantity is determined by the electricity quantity decomposition ratio for each period;

[0089] Use optimization algorithms to optimize the distribution of the supply of medium- and long-term contracts through a dynamic adjustment mechanism: use a rolling optimization method, with time nodes as units, set k as the current calculation time node, and the objective function is to minimize the deviation between supply and demand:

[0090]

[0091] Where: S k ′ is the supply after adjustment at time node k; D k ′ is the actual demand at time node k; T is the number of time cycle nodes for rolling optimization;

[0092] The constraints are:

[0093]

[0094] Where: S is the total supply; The minimum supply for each period.

[0095] S102: Obtaining price data of the medium- and long-term market and price data of the spot market, inputting the price data of the medium- and long-term market and the price data of the spot market into a pre-established vector autoregression VAR model, and outputting a prediction result of the dynamic relationship between the prices of the medium- and long-term market and the spot market;

[0096] After signing a medium- and long-term contract, the medium- and long-term market implements a supporting physical security mechanism, including:

[0097] Reliability contracts that oblige generators to provide physical power delivery under pre-set conditions and subject them to fines or other penalties if they fail to meet the contract;

[0098] Establish backup power generation mechanisms, including fast-start gas turbines or energy storage systems;

[0099] Establish and improve a balancing market mechanism to ensure that system operators can dispatch resources in real time to maintain system balance and stability. The balancing market dispatches power generation through price signals to ensure real-time supply and demand balance.

[0100] The price data of the medium and long-term markets and the price data of the spot market are preprocessed before being input into the model, including cleaning and standardization of the data.

[0101] Among them, the VAR model is used to estimate the dynamic relationship between the medium- and long-term market and the spot market prices. The vector autoregression (VAR) model is a statistical model used to capture the dynamic relationship between multivariate time series. In the electricity market, the VAR model can be used to describe the relationship between the medium- and long-term market prices and the spot market prices. The mathematical form of the VAR model is as follows:

[0102]

[0103] Where: y t is a vector containing multiple variables, including medium- and long-term market prices and spot market prices; c is a constant vector; A i is the coefficient matrix of lag period i; u t is the error term vector. By estimating the VAR model, we can obtain the dynamic relationship between the medium- and long-term market prices and the spot market prices, thereby achieving price linkage.

[0104] S103: Inputting the price data of the medium and long-term market and the price data of the spot market into the pre-established generalized autoregressive conditional heteroskedastic GARCH model, and outputting the conditional volatility of the price;

[0105] The conditional volatility of prices is estimated through the GARCH model. The generalized autoregressive conditional heteroskedasticity (GARCH) model is a method used to describe the conditional volatility in time series data. In the electricity market, spot market prices are usually volatile. The use of the GARCH model can better capture price volatility, thereby improving the accuracy of the price linkage mechanism. The mathematical form of the GARCH (1,1) model is as follows:

[0106]

[0107] Where: is the conditional volatility at time t; ω, α and β are the model parameters to be estimated; ∈ t-1 is the residual at time t-1. Through the GARCH model, the volatility of spot market prices can be estimated and incorporated into the linkage mechanism of medium and long-term contract prices.

[0108] S104: Determine the instantaneous supply and demand situation of the spot market based on the forecast results of the dynamic relationship between the medium- and long-term market and the spot market prices and the volatility of prices, decompose the electricity volume by combining the instantaneous supply and demand situation of the spot market with the medium- and long-term contracts, and realize the coordinated dispatch of market electricity volume.

[0109] Determine the signing ratio of medium- and long-term contracts based on market demand and supply; regularly monitor market operation and adjust the signing ratio based on actual conditions.

[0110] Through price linkage, the prices of medium- and long-term contracts can reflect the real-time supply and demand conditions of the spot market, enhancing the market's predictability and adaptability, including data collection, model building, price adjustment and monitoring feedback.

[0111] The initial electricity consumption is decomposed according to the electricity consumption decomposition ratio, and a dynamic adjustment mechanism is introduced to adjust the supply according to real-time demand changes to improve the responsiveness and flexibility of the market.

[0112] By establishing a capacity market, signing reliability contracts, setting up a backup power generation mechanism and implementing demand response, we can ensure that power generation companies provide sufficient electricity in the spot market and achieve coordinated dispatch of market electricity.

[0113] Implement a credit rating system for market entities, promote performance bonds or insurance mechanisms, urge market entities to fulfill medium- and long-term contracts, and reduce market operating risks.

[0114] Embodiment 2: In the second aspect, as Figure 3 As shown, in order to achieve the above-mentioned purpose, the present invention discloses an electric power dispatching system for medium- and long-term electric power market and spot market, comprising:

[0115] The power decomposition module 11 is used to receive the initial medium- and long-term contract power, decompose the initial medium- and long-term contract power through the medium- and long-term curve decomposition mechanism, and obtain the medium- and long-term contract decomposition power;

[0116] The price prediction module 12 is used to obtain the price data of the medium and long-term market and the price data of the spot market, input the price data of the medium and long-term market and the price data of the spot market into a pre-established vector autoregression VAR model, and output the prediction result of the dynamic relationship between the medium and long-term market and the spot market prices;

[0117] The conditional volatility module 13 is used to input the price data of the medium and long-term market and the price data of the spot market into a pre-established generalized autoregressive conditional heteroskedasticity GARCH model, and output the conditional volatility of the price;

[0118] The electricity dispatch module 14 is used to determine the instantaneous supply and demand situation of the spot market according to the forecast results of the dynamic relationship between the medium- and long-term market and the spot market prices and the price volatility, and to decompose the electricity quantity of the spot market by combining the instantaneous supply and demand situation of the spot market with the medium- and long-term contracts to realize the coordinated dispatch of the market electricity quantity.

[0119] In conjunction with the second aspect, in some implementations of the second aspect, the system further includes: the power decomposition module 11 decomposes the initial medium- and long-term contract power through the medium- and long-term curve decomposition mechanism, including:

[0120] Collect historical electricity consumption data and power generation capacity data to identify peak and trough electricity consumption;

[0121] Determine the benchmark supply curve. The decomposition principles include smoothing the power supply curve, allocating the power supply in each period, and reserving adjustment space. The decomposition of power in each period is performed using the following formula:

[0122] Q i =α i Q c

[0123] Where: Q i Decompose the power consumption for each time period; α i Q is the power decomposition ratio; c The total contracted electricity quantity is determined by the electricity quantity decomposition ratio for each period;

[0124] The electricity decomposition module 11 uses an optimization algorithm to optimize the supply allocation of medium- and long-term contracts through a dynamic adjustment mechanism: a rolling optimization method is used, with time nodes as units, k is set as the current calculation time node, and the objective function is to minimize the deviation between supply and demand:

[0125]

[0126] Where: S k ′ is the supply after adjustment at time node k; D k ′ is the actual demand at time node k; T is the number of time cycle nodes for rolling optimization;

[0127] The constraints are:

[0128]

[0129] Where: S is the total supply; The minimum supply for each period;

[0130] Price Prediction Module 12 After signing a medium- and long-term contract, the supporting physical security mechanism is implemented, including:

[0131] Reliability contracts that oblige generators to provide physical power delivery under pre-set conditions and subject them to fines or other penalties if they fail to meet the contract;

[0132] Establish backup power generation mechanisms, including fast-start gas turbines or energy storage systems;

[0133] Establish and improve a balancing market mechanism to ensure that system operators can dispatch resources in real time to maintain system balance and stability, where the balancing market dispatches power generation through price signals to ensure real-time supply and demand balance;

[0134] The price prediction module 12 pre-processes the price data of the medium and long-term market and the price data of the spot market before inputting into the model, including cleaning and standardizing the data;

[0135] The price prediction module 12 inputs the price data of the medium and long-term market and the price data of the spot market into the calculation formula of the pre-established vector autoregression VAR model, including:

[0136]

[0137] Where: y t is a vector containing multiple variables, including medium- and long-term market prices and spot market prices; c is a constant vector; A i is the coefficient matrix of lag period i; u t is the error term vector;

[0138] The calculation process of the conditional volatility module 13 inputting the price data of the medium and long-term market and the price data of the spot market into the pre-established generalized autoregressive conditional heteroskedasticity GARCH model includes:

[0139]

[0140] Where: is the conditional volatility at time t; ω, α and β are the model parameters to be estimated; ∈ t-1 is the residual at time t-1;

[0141] The power dispatch module 14 realizes the process of coordinated dispatch of market power:

[0142] Determine the signing ratio of medium- and long-term contracts based on market demand and supply; regularly monitor market operation and adjust the signing ratio based on actual conditions;

[0143] Through price linkage, the prices of medium- and long-term contracts reflect the real-time supply and demand conditions of the spot market, including data collection, model building, price adjustment and monitoring feedback;

[0144] Perform initial power decomposition according to the power decomposition ratio, and introduce a dynamic adjustment mechanism to adjust the supply according to real-time demand changes;

[0145] By establishing a capacity market, signing reliability contracts, setting up a backup power generation mechanism and implementing demand response, we can ensure that power generation companies can provide electricity in the spot market and achieve coordinated dispatch of market electricity.

[0146] Example:

[0147] Taking the annual electricity decomposition as an example, the annual electricity is decomposed into 12 months through the decomposition mechanism. The monthly electricity demand data is [750, 650, 700, 850, 950, 1050, 1150, 1250, 1050, 950, 850, 800] (MWh), the total supply is 10900MWh, and the initial supply is solved by the monthly decomposition ratio.

[0148] In actual applications, monthly demand is affected by many factors, such as weather changes, economic activities, etc. Therefore, the actual demand will introduce random changes on the basis of the predicted demand to reflect these uncertainties. The actual demand changes randomly, and on this basis, the rolling optimization solution model is used to adjust the initial supply. The final solution result is shown in Table 1.

[0149] Table 1

[0150]

[0151]

[0152] In months when demand increases / decreases, the adjusted supply also increases / decreases accordingly to meet actual demand; the optimized electricity volume has significant changes compared to the initial decomposed electricity volume, indicating that the adjusted supply volume can respond to changes in actual demand in a timely manner and ensure the reliability of power supply.

[0153] In order to study the price linkage mechanism between the medium- and long-term market and the spot market, the medium- and long-term contract prices and spot market price data of a certain region for 100 days are selected. Figure 5 As shown:

[0154] In actual applications, spot market prices are affected by many factors, resulting in greater volatility. In order to simulate this volatility, the GARCH model is used to more accurately capture the volatility of spot market prices and provide a reference for the linkage of medium- and long-term contract prices. The specific steps include: combining historical data and using the VAR model to predict future spot prices; based on the predicted results of spot market prices, calculating the adjustment value of medium- and long-term contract prices to achieve price linkage.

[0155] The contract base price is set to 340 yuan / MWh, and the price linkage coefficient is set to 0.5, that is, the responsiveness of the medium- and long-term contract price to the change of the spot market price is 50%, the lag order is 1, and the number of prediction steps is 10. The final results are shown in Table 2:

[0156] Table 2

[0157]

[0158] As can be seen from the table, conditional volatility reflects the uncertainty of market prices. The fluctuation of spot price forecast results is small, remaining between 342.49 and 344.69 yuan / MWh, which is relatively stable in the short term. The fluctuation range of medium- and long-term contract prices is smaller than that of spot market prices, indicating that the price linkage mechanism can smooth the short-term fluctuations of the spot market, and the medium- and long-term contract prices can dynamically respond to changes in spot market prices. The change trends of the two are consistent, reducing the risks brought by price fluctuations.

[0159] Based on the same inventive concept, the present invention also provides a computer device, which includes: one or more processors, and a memory for storing one or more computer programs; the program includes program instructions, and the processor is used to execute the program instructions stored in the memory. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is used to implement one or more instructions, specifically for loading and executing one or more instructions in a computer storage medium to implement the above method.

[0160] It needs to be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium, on which a computer program is stored, and the computer program is executed by a processor to execute the above method. The storage medium can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0161] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0162] The above shows and describes the basic principles, main features and advantages of the present disclosure. Those skilled in the art should understand that the present disclosure is not limited by the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present disclosure. Without departing from the spirit and scope of the present disclosure, the present disclosure may have various changes and improvements, and these changes and improvements fall within the scope of the present disclosure to be protected.

Claims

1. A method for dispatching electricity in the medium- and long-term electricity market and the spot market, characterized in that: The method comprises the following steps: Receive the initial medium- and long-term contract electricity, decompose the initial medium- and long-term contract electricity through the medium- and long-term curve decomposition mechanism, and obtain the medium- and long-term contract decomposed electricity; Obtaining price data of the medium- and long-term market and price data of the spot market, inputting the price data of the medium- and long-term market and price data of the spot market into a pre-established vector autoregression VAR model, and outputting a prediction result of the dynamic relationship between the prices of the medium- and long-term market and the spot market; Input the price data of the medium and long-term market and the price data of the spot market into the pre-established generalized autoregressive conditional heteroskedastic GARCH model, and output the conditional volatility of the price; Based on the forecast results of the dynamic relationship between the medium- and long-term market and the spot market prices and the volatility of prices, the instant supply and demand situation of the spot market is determined, and the instant supply and demand situation of the spot market is combined with the medium- and long-term contracts to decompose the electricity volume, so as to achieve coordinated scheduling of market electricity volume.

2. The method for dispatching electricity in the medium- and long-term electricity market and the spot market according to claim 1 is characterized in that: The decomposition of the initial medium- and long-term contract quantity through the medium- and long-term curve decomposition mechanism includes: Collect historical electricity consumption data and power generation capacity data to identify peak and trough electricity consumption; Determine the benchmark supply curve. The decomposition principles include smoothing the power supply curve, allocating the power supply in each period, and reserving adjustment space. The decomposition of power in each period is performed using the following formula: Q i =α i ·Q c Where: Q i Decompose the power consumption for each time period; α i Q is the power decomposition ratio; c The total contracted electricity volume is determined by the electricity volume decomposition ratio for each period.

3. The method for dispatching electricity in the medium- and long-term electricity market and the spot market according to claim 2 is characterized in that: Use optimization algorithms to optimize the distribution of the supply of medium- and long-term contracts through a dynamic adjustment mechanism: use a rolling optimization method, with time nodes as units, set k as the current calculation time node, and the objective function is to minimize the deviation between supply and demand: Where: S k ′ is the supply after adjustment at time node k; D k ′ is the actual demand at time node k; T is the number of time cycle nodes for rolling optimization; The constraints are: Where: S is the total supply; The minimum supply for each period.

4. The method for dispatching electricity in the medium- and long-term electricity market and the spot market according to claim 1 is characterized in that: After the mid- to long-term contracts are signed in the mid- to long-term market, a supporting physical security mechanism will be implemented, including: Reliability contracts that oblige generators to provide physical power delivery under pre-set conditions and subject them to fines or other penalties if they fail to meet the contract; Establish backup power generation mechanisms, including fast-start gas turbines or energy storage systems; Establish and improve a balancing market mechanism to ensure that system operators can dispatch resources in real time to maintain system balance and stability. The balancing market dispatches power generation through price signals to ensure real-time supply and demand balance.

5. The method for dispatching electricity in the medium- and long-term electricity market and the spot market according to claim 1 is characterized in that: The price data of the medium and long-term markets and the price data of the spot market are preprocessed before being input into the model, including cleaning and standardizing the data.

6. The method for dispatching electricity in the medium- and long-term electricity market and the spot market according to claim 1 is characterized in that: The calculation formula for inputting the price data of the medium and long-term market and the price data of the spot market into the pre-established vector autoregression VAR model includes: Where: y t is a vector containing multiple variables, including medium- and long-term market prices and spot market prices; c is a constant vector; A i is the coefficient matrix of lag period i; u t is the error term vector.

7. A method for dispatching electricity in the medium- and long-term electricity market and the spot market according to claim 6, characterized in that: The calculation process of inputting the price data of the medium and long-term market and the price data of the spot market into the pre-established generalized autoregressive conditional heteroskedastic GARCH model includes: Where: is the conditional volatility at time t; ω, α and β are the model parameters to be estimated; ∈ t-1 is the residual at time t-1.

8. The method for dispatching electricity in the medium- and long-term electricity market and the spot market according to claim 1 is characterized in that: The process of achieving coordinated dispatch of market electricity: Determine the signing ratio of medium- and long-term contracts based on market demand and supply; regularly monitor market operation and adjust the signing ratio based on actual conditions; Through price linkage, the prices of medium- and long-term contracts reflect the real-time supply and demand conditions of the spot market, including data collection, model building, price adjustment and monitoring feedback; Perform initial power decomposition according to the power decomposition ratio, and introduce a dynamic adjustment mechanism to adjust the supply according to real-time demand changes; By establishing a capacity market, signing reliability contracts, setting up a backup power generation mechanism and implementing demand response, we can ensure that power generation companies can provide electricity in the spot market and achieve coordinated dispatch of market electricity.

9. An electricity dispatching system for the medium- and long-term electricity market and the spot market, characterized in that: include: The power decomposition module is used to receive the initial medium- and long-term contract power, decompose the initial medium- and long-term contract power through the medium- and long-term curve decomposition mechanism, and obtain the medium- and long-term contract decomposition power; The price prediction module is used to obtain the price data of the medium- and long-term market and the price data of the spot market, input the price data of the medium- and long-term market and the price data of the spot market into a pre-established vector autoregression VAR model, and output the prediction result of the dynamic relationship between the medium- and long-term market and the spot market prices; The conditional volatility module is used to input the price data of the medium and long-term markets and the price data of the spot market into the pre-established generalized autoregressive conditional heteroskedastic GARCH model, and output the conditional volatility of the price; The electricity dispatch module is used to determine the instant supply and demand situation of the spot market based on the forecast results of the dynamic relationship between the medium- and long-term market and the spot market prices and the volatility of prices, and to decompose the electricity volume by combining the instant supply and demand situation of the spot market with the medium- and long-term contracts to achieve coordinated dispatch of market electricity.

10. The power dispatching system for the medium- and long-term power market and the spot market according to claim 9 is characterized in that: The power decomposition module decomposes the initial medium- and long-term contract power through the medium- and long-term curve decomposition mechanism, including: Collect historical electricity consumption data and power generation capacity data to identify peak and trough electricity consumption; Determine the benchmark supply curve. The decomposition principles include smoothing the power supply curve, allocating the power supply in each period, and reserving adjustment space. The decomposition of power in each period is performed using the following formula: Q i =α i ·Q c Where: Q i Decompose the power consumption for each time period; α i Q is the power decomposition ratio; c The total contracted electricity quantity is determined by the electricity quantity decomposition ratio for each period; The power decomposition module uses an optimization algorithm to optimize the supply allocation of medium- and long-term contracts through a dynamic adjustment mechanism: a rolling optimization method is used, with time nodes as units, k is set as the current calculation time node, and the objective function is to minimize the deviation between supply and demand: Where: S k ′ is the supply after adjustment at time node k; D k ′ is the actual demand at time node k; T is the number of time cycle nodes for rolling optimization; The constraints are: Where: S is the total supply; The minimum supply for each period; After the mid- to long-term contract is signed in the price prediction module mid- to long-term market, a supporting physical security mechanism will be implemented, including: Reliability contracts that oblige generators to provide physical power delivery under pre-set conditions and subject them to fines or other penalties if they fail to meet the contract; Establish backup power generation mechanisms, including fast-start gas turbines or energy storage systems; Establish and improve a balancing market mechanism to ensure that system operators can dispatch resources in real time to maintain system balance and stability, where the balancing market dispatches power generation through price signals to ensure real-time supply and demand balance; The price data of the medium- and long-term markets and the price data of the spot market in the price prediction module are preprocessed before being input into the model, including cleaning and standardizing the data; The price prediction module inputs the price data of the medium and long-term market and the price data of the spot market into the pre-established vector autoregression VAR model. The calculation formula includes: Where: y t is a vector containing multiple variables, including medium- and long-term market prices and spot market prices; c is a constant vector; A i is the coefficient matrix of lag period i; u t is the error term vector; The conditional volatility module inputs the price data of the medium and long-term market and the price data of the spot market into the pre-established generalized autoregressive conditional heteroskedasticity GARCH model. The calculation process includes: Where: is the conditional volatility at time t; ω, α and β are the model parameters to be estimated; ∈ t-1 is the residual at time t-1; The power dispatch module realizes the process of coordinated dispatch of market power: Determine the signing ratio of medium- and long-term contracts based on market demand and supply; regularly monitor market operation and adjust the signing ratio based on actual conditions; Through price linkage, the prices of medium- and long-term contracts reflect the real-time supply and demand conditions of the spot market, including data collection, model building, price adjustment and monitoring feedback; Perform initial power decomposition according to the power decomposition ratio, and introduce a dynamic adjustment mechanism to adjust the supply according to real-time demand changes; By establishing a capacity market, signing reliability contracts, setting up a backup power generation mechanism and implementing demand response, we can ensure that power generation companies can provide electricity in the spot market and achieve coordinated dispatch of market electricity.

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