Electric power spot transaction market quantity reporting method

Abstract

The invention discloses an electric power spot transaction market quantity reporting method, and relates to the technical field of electric power transaction, and the system comprises an energy storage intelligent management and collection module, an electric power supply and demand prediction module, a transaction rule building module, a transaction optimization and regulation module, and a market supervision and model optimization module. A refined power supply and demand prediction model is created by collecting various data sources including meteorological data, macroeconomic data and industrial project planning information and combining historical power supply and demand data, power demands in different seasons and different time periods can be accurately predicted, the prediction result can be dynamically corrected according to real-time data, and the prediction efficiency is improved. And meanwhile, a dynamic energy storage excitation mechanism is also constructed, and a cross-seasonal energy storage strategy is formulated in combination with cost, price fluctuation and technical characteristics, so that reasonable configuration and efficient utilization of energy storage resources are effectively realized, and the stability and economical efficiency of the power system are further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power trading, and specifically provides a method for reporting power quantities in the electricity spot trading market. Background Art

[0002] With the continuous development and improvement of the power market, electricity spot trading has become an important part of the power market. The effective operation of the electricity spot trading market is of great significance for ensuring the stability of power supply, promoting the optimal allocation of power resources, and driving the transformation of the energy structure. In the electricity spot trading market, energy storage technology, as an important means to balance power supply and demand and improve the flexibility of the power system, is increasingly widely used. At the same time, with the rapid development of big data and Internet of Things technologies, it provides strong support for the intelligent and refined management of the electricity spot trading market.

[0003] However, the traditional electricity spot trading market model system still has many deficiencies in dealing with the complex and changeable power supply and demand situation. On the one hand, the traditional system often relies on the simple statistics and analysis of historical data in power supply and demand forecasting, lacking in-depth exploration of the internal laws of power supply and demand in different seasons and different time periods, resulting in inaccurate forecasting results and being difficult to effectively guide the decision-making and dispatching of electricity spot trading. On the other hand, the traditional system lacks dynamics and flexibility in the management and utilization of energy storage resources, failing to fully consider factors such as cost, price fluctuations, and technical characteristics, leading to low utilization efficiency of energy storage resources and being unable to fully play its role in balancing power supply and demand and improving the stability of the power system. In addition, the traditional system also has many deficiencies in trading rule formulation, trading process design, and market supervision, making it difficult to meet the needs of the rapid development of the electricity spot trading market.

[0004] Therefore, developing a method for reporting power quantities in the electricity spot trading market will help improve the stability of the power market, promote the optimal allocation of power resources, and drive the sustainable development of the power industry. Summary of the Invention

[0005] The purpose of the present invention is to make up for the deficiencies of the existing technology and provide a method for reporting power quantities in the electricity spot trading market. By comprehensively applying big data analysis, intelligent forecasting, and optimization algorithms, the system realizes the forecasting and dynamic balance of power supply and demand. Through energy storage information management and cross-season energy storage strategies, it effectively improves the energy utilization efficiency and the flexibility of power supply. At the same time, the trading rule construction module and the trading optimization guidance mechanism ensure the fairness, efficiency, and transparency of trading, promoting the healthy development of the power market. In addition, the establishment of the market supervision evaluation system provides a strong guarantee for the stable operation of the market.

[0006] To solve the above technical problems, the present invention provides the following technical solution: A method for reporting electricity quantities in a spot electricity trading market. The system includes: an energy storage intelligent management and acquisition module, an electricity supply and demand prediction module, a trading rule construction module, a trading optimization and regulation module, and a market supervision and model optimization module;

[0007] The energy storage intelligent management and acquisition module: collects technical parameters, geographical location information, and information of the affiliated operating entity of compressed air energy storage and molten salt energy storage, and constructs an information database by monitoring the operating status in real time, collects historical electricity supply and demand data, analyzes the internal laws of electricity supply and demand in different seasons and different time periods to create an electricity supply and demand prediction model, and formulates a cross-season energy storage strategy in combination with the output results of the electricity supply and demand prediction model;

[0008] The electricity supply and demand prediction module: collects data on meteorological data, macroeconomic data, and industrial project planning information, analyzes its impact on electricity supply and demand data, optimizes the electricity supply and demand prediction model, sets a warning threshold for supply and demand changes according to the prediction results of the optimized electricity supply and demand prediction model. When it is predicted that the electricity supply and demand situation will change significantly in a certain season or time period and exceed the set threshold, a warning signal is triggered, and a risk assessment is carried out on the warning supply and demand change situation, analyzing its impact on the stability of the power system, the price fluctuation of the electricity market, and the electricity supply, and adjusting the subsequent trading plan and starting emergency measures according to the risk assessment results;

[0009] The trading rule construction module: formulates cross-season electricity energy storage spot trading rules, clarifies the rule terms of the access conditions, trading processes, and settlement methods for energy storage resources to participate in trading, designs a cross-season electricity energy storage spot trading contract covering trading electricity quantities, prices, time windows, energy storage resource quality technical standards, and liability for breach of contract terms. During the peak electricity demand season, energy storage resource owners and demanders prepare and declare according to the rules and contracts, and after being matched and signed by the trading platform, execute the trading and monitor energy storage dispatching and power transmission, and settle and liquidate according to the agreement;

[0010] The trading optimization and regulation module: takes the goal of maximizing the overall operating efficiency of the power system, constructs a trading plan optimization mathematical model by integrating the benefits of energy storage resources, the benefits of power supply reliability, the benefits of market trading activity, and environmental benefits, sets the power supply and demand balance, energy storage capacity limit, and grid transmission capacity limit as constraints, solves using the genetic algorithm, and issues the generated trading plan to relevant entities in real time. Through the Internet of Things technology and intelligent metering devices, the actual charge and discharge electricity quantities, power, and trading execution time data of energy storage resources are collected in real time and compared with the expected values of the trading plan to calculate the execution deviation rate and execution progress indicators, and the trading plan is dynamically adjusted according to the monitoring feedback information and market changes;

[0011] The market supervision and model optimization module: constructs a multi-level power spot trading market supervision framework covering legal norms, administrative supervision, industry self-discipline, and social supervision, and formulates rules; establishes an effectiveness evaluation index system with multiple dimensions including resource utilization efficiency, market operation efficiency, power system stability, and market participant satisfaction, and comprehensively evaluates the operation effectiveness of the power spot trading market model regularly by an evaluation method combining quantitative analysis and qualitative analysis, and formulates targeted improvement suggestions and optimization measures based on the evaluation results.

[0012] Furthermore, in the energy storage intelligent acquisition module, a power supply and demand prediction model is created according to historical power supply and demand data through the power demand comprehensive prediction formula. Let the power demand prediction value be D. p , the average power demand in the same historical period is , the seasonal influence factor is S i , the time trend coefficient is T t , the influence value of sudden factors is U f , then the power demand prediction formula is:

[0013] Even further, in the energy storage intelligent acquisition module, a cross-seasonal energy storage strategy is formulated by comprehensively quantifying and evaluating different new energy storage resources through the energy storage resource evaluation and selection formula. Let the comprehensive evaluation index of the energy storage resource be S, the energy storage capacity be D, the charge and discharge efficiency be η, the energy storage cost be K, the expected service life be L, the reliability factor be R, the market demand response flexibility factor be F, and the power demand prediction value be D. p , the power supply capacity be P s , the importance factor of power supply and demand balance be B i , the formula is:

[0014] Even further, in the power supply and demand prediction module, the original power demand prediction value is corrected by multiple factors through the power supply and demand prediction correction formula. Let the original power demand prediction value be D. p , the meteorological influence factor be W, the economic development influence factor be E, the industrial project change influence factor be I, and the time trend correction factor be T. The calculation formula is: D = D p ×(1 + W + E + I + T).

[0015] Even further, the warning threshold for supply and demand changes in the power supply and demand prediction module:

[0016] The setting of the power demand warning threshold: Let the power demand warning threshold be D. th , the average value of the highest daily power demand in the same historical period be Then the power demand warning threshold is: When the predicted power demand value D optWhen the threshold is exceeded, a power demand warning signal is triggered;

[0017] The power supply warning threshold is set as follows: for the power generation capacity, let the total power generation capacity of various power generation resources be P total , and the safety margin coefficient is α, then the power supply warning threshold is: P th =(1 - α)×P total , when the predicted power demand exceeds the power supply warning threshold, a power supply warning signal is triggered.

[0018] Furthermore, the emergency measures in the power supply and demand prediction module are as follows:

[0019] The emergency measures for demand - side management of electricity: according to the pre - formulated shift plan, arrange some industrial enterprises to suspend production and give up electricity during peak hours; implement off - peak operation management for air - conditioning and lighting electrical equipment in commercial places; implement stepped electricity price adjustment or time - of - use electricity price preferential policies for residential users to encourage residents to reduce electricity consumption during peak hours and relieve the power supply pressure;

[0020] The emergency measures for the power supply side: start standby generating units, and at the same time, optimize the dispatching of power generation resources to ensure the power supply for hospitals, communication hubs, transportation hubs, urban central areas, and industrial parks.

[0021] Furthermore, in the transaction optimization and regulation module, for the optimized mathematical model of the transaction plan, let the overall operating efficiency of the power system be U, the income of energy storage resources be I s , the power supply reliability benefit be I r , the market transaction activity benefit be I a , the environmental benefit be I e , and the set of constraint condition functions be G, including power supply - demand balance, energy storage capacity limit, and power grid transmission limit. The formula is: U = max(I s +I r +I a +I e ); s.t.G.

[0022] Furthermore, the specific content of the set of constraint condition functions G in the transaction optimization and regulation module is as follows:

[0023] Energy storage capacity limit: for each energy storage resource point i, let its stored electricity be E i , the minimum storage capacity be E i,min , and the maximum storage capacity be: E i，max , the energy storage capacity limit constraint condition is: E i,min ≤E i ≤E i,max , and at the same time, let the charging and discharging power of the energy storage resource, the charging power P i,charge ≤Pi,charge,max , the discharge power P i,discharge ≤P i,discharge,max ;

[0024] Grid transmission capacity limit: Let the transmission power of each transmission line j in the grid be P j , and the maximum transmission power be P j,max , then the grid transmission capacity limit constraint is: P j ≤P j,max ;

[0025] Power supply - demand balance: Let the power supply include the power generation P gen of power generation resources, the discharge amount Q discharge of energy storage resources, and the power import volume I import , and the power demand includes the conventional electricity load L, the charging demand Q charge of energy storage resources, and the power export volume I export , which is expressed as: P gen +Q discharge +I import =L + Q charge +I export .

[0026] Furthermore, the steps of using the genetic algorithm to solve the trading plan optimization problem constructed by integrating multiple benefits in the trading optimization and control module are as follows: encoding the trading plan variables, randomly initializing the population, using the objective function related to the comprehensive benefit as the fitness function, selecting individuals through roulette wheel selection operation, performing crossover and mutation operations on the selected individuals, and after the operations, updating the population to judge whether the maximum iteration number T MAX is reached. If the maximum iteration number is reached, terminate the iteration and output the trading plan scheme corresponding to the individual with the highest fitness value in the current population as the optimal solution. If not, continue the above crossover and mutation operations.

[0027] Compared with the prior art, this method for reporting quantities in the electricity spot trading market has the following beneficial effects:

[0028] First, by collecting multiple data sources, including meteorological data, macro - economic data, and industrial project planning information, and combining historical power supply - demand data, the present invention creates a refined power supply - demand prediction model. It can not only accurately predict the power demand in different seasons and different time periods, but also dynamically correct the prediction results according to real - time data, improving the accuracy and timeliness of the prediction. At the same time, the present invention also constructs a dynamic energy storage incentive mechanism, formulates cross - seasonal energy storage strategies by combining costs, price fluctuations, and technical characteristics, effectively realizes the reasonable allocation and efficient utilization of energy storage resources, and further improves the stability and economy of the power system.

[0029] Second, aiming at maximizing the overall operational efficiency of the power system, the present invention constructs an optimization mathematical model for trading plans by comprehensively considering multiple dimensions such as the income of energy storage resources, the benefit of power supply reliability, the benefit of market trading activity, and the environmental benefit. The genetic algorithm is used to solve this model to generate the optimal trading plan. Key data is collected in real time and compared with the expected values for analysis, and the trading plan is dynamically adjusted to ensure the efficient execution of transactions and the stable operation of the power system. The present invention also constructs a multi-level regulatory framework for the power spot trading market covering legal norms, administrative supervision, industry self-discipline, and social supervision, and formulates an effectiveness evaluation index system covering multiple dimensions such as resource utilization efficiency, market operation efficiency, power system stability, and market participant satisfaction, providing a strong guarantee for the healthy development and continuous optimization of the market.

[0030] Other advantages, objectives, and features of the present invention will be described to some extent in the subsequent specification, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be learned from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0032] Figure 1 It is a flowchart of a method for reporting quantities in a power spot trading market;

[0033] Figure 2 It is a process framework diagram of a method for reporting quantities in a power spot trading market. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention objective, the following, in combination with the accompanying drawings and preferred embodiments, details the specific embodiments, structures, features, and their effects of the present invention as follows.

[0035] Embodiment 1

[0036] Operation of the power spot trading market during the peak summer electricity consumption period

[0037] Collect the technical parameters of compressed air energy storage and molten salt energy storage in a certain area, such as the gas storage pressure range of compressed air energy storage and the melting point temperature of molten salt energy storage; geographical location information to determine the location of the energy storage facility; information on the affiliated operation entity to clarify the responsible entity, and monitor the operating status in real time, such as the current energy storage power and charge-discharge power, to construct an information database.

[0038] Collect historical summer electricity supply and demand data. It is found through analysis that the electricity demand of downstream industries is high during the day in summer, and the electricity demand of residents increases at night but is relatively stable. Moreover, the air-conditioning electricity load is closely related to the temperature. Create an electricity supply and demand prediction model, and set the predicted value of electricity demand as D p , and the average value of electricity demand in the same historical period is The seasonal impact factor is S i , and the time trend coefficient is T t , and the impact value of sudden factors is U f , then the electricity demand prediction formula is: It is predicted that the electricity demand in summer will increase significantly during high-temperature periods. Combining the prediction results and considering the long duration of the summer electricity peak, formulate a cross-season energy storage strategy. Choose to store more electric energy during the low electricity price period in spring, and give priority to using molten salt energy storage technology with high charge-discharge efficiency and relatively low energy storage cost. Conduct a comprehensive quantitative evaluation of different new energy storage resources through the energy storage resource evaluation and selection formula. Set the comprehensive evaluation index of energy storage resources as S, the energy storage capacity as D, the charge-discharge efficiency as η, the energy storage cost as K, the expected service life as L, the reliability factor as R, the market demand response flexibility factor as F, and the predicted value of electricity demand as D p , the power supply capacity is P s , and the importance factor of power supply and demand balance is B i , and the formula is:

[0039] Collect summer meteorological data (such as temperature, humidity), macroeconomic data (such as local industrial added value, business activity activity), and industrial project planning information (such as whether there are new high-energy-consuming enterprises put into production). It is found through analysis that for every 1°C increase in temperature, the electricity demand increases by a certain proportion. The growth of industrial added value drives the increase in industrial electricity demand, and the commissioning of new high-energy-consuming enterprises will significantly increase the electricity demand. Optimize the electricity supply and demand prediction model according to these influencing factors, and adopt the electricity supply and demand prediction correction formula. Set the original predicted value of electricity demand as D p , the meteorological impact factor is W, the economic development impact factor is E, the industrial project change impact factor is I, and the time trend correction factor is T. The calculation formula is: D = D p ×(1 + W + E + I + T), and a more accurate predicted value of electricity demand is obtained.

[0040] Set the warning threshold for supply and demand changes. The electricity demand warning threshold setting: Set the electricity demand warning threshold as D th , and the average value of the highest daily electricity demand in the same historical period is Then the electricity demand warning threshold is: When the predicted electricity demand value D optWhen it exceeds this threshold, an early warning signal for power demand is triggered. The setting of the power supply early warning threshold: For the power generation capacity, let the total power generation capacity of various power generation resources be P total , and the safety margin coefficient be α. Then the power supply early warning threshold is: P th =(1 - α)×P total , when the predicted power demand exceeds the power supply early warning threshold, it indicates that the power supply is facing a tense situation. An early warning signal for power supply is triggered, and a risk assessment is carried out on the supply - demand changes of the early warning. It is found that a large increase in power demand may lead to a decline in the stability of the power system, an intensification of power market price fluctuations, and power supply shortages in some areas. According to the risk assessment results, the subsequent trading plan is adjusted, such as increasing the discharge volume of energy storage resources, urgently transporting power resources from other regions, and at the same time formulating emergency measures, such as arranging some industrial enterprises to stagger production peaks and implementing time - of - use electricity prices for residents to encourage them to stagger electricity consumption.

[0041] Formulate cross - seasonal power energy storage spot trading rules, and clarify the key rule terms such as the access conditions for energy storage resources to participate in trading (such as the technical standards and safety performance of energy storage facilities), trading processes (including declaration, matching, signing, execution, and settlement links), and settlement methods (such as settlement according to real - time electricity prices or in the way of fixed price plus floating price).

[0042] Design cross - seasonal power energy storage spot trading contracts, covering trading electricity volume (clarify the range of electricity volume that energy storage resources should release during peak summer hours), price (determine a reasonable price according to market supply and demand and costs), time window (stipulate that energy storage resources must ensure power supply during specific high - temperature hours in summer), quality and technical standards of energy storage resources (such as the guarantee rate of energy storage electricity volume and requirements for charge - discharge efficiency), and liability - for - breach clauses (stipulate punitive compensation for the party that fails to fulfill its obligations according to the contract).

[0043] During the peak season of summer power demand, energy storage resource owners and demanders (such as power retailers and large industrial users) prepare relevant materials and declare trading intentions according to the rules and contracts. After being matched and signed by the trading platform, the transaction is strictly executed. The trading platform monitors the energy storage dispatch in real - time (ensuring that energy storage resources discharge according to the plan) and power transmission (ensuring the safe and stable transmission of power to the demander), and conducts settlement and liquidation according to the agreement to ensure the fairness and justice of the transaction.

[0044] With the goal of maximizing the overall operating efficiency of the power system, the comprehensive income of energy storage resources is I s , the reliability benefit of power supply is I r , the activity benefit of market trading is I a , and the environmental benefit is I eBuild an optimization mathematical model for the trading plan. Let the overall operating efficiency of the power system be U, and the set of constraint functions be G, including power supply-demand balance, energy storage capacity limit, and power grid transmission limit. The formula is: U = max(I s +I r +I a +I e ); s.t. G, where the specific content of the set of constraint functions G is as follows: Energy storage capacity limit: For each energy storage resource point i, let its stored power be E i , the minimum storage capacity be E i，min , and the maximum storage capacity be: E i，max . The energy storage capacity limit constraint is: E i，min ≤E i ≤E i，max . At the same time, let the charging and discharging power of the energy storage resource, the charging power P i，charge ≤P i，charge，max , and the discharging power P i，discharge ≤P i，discharge，max ; Power grid transmission capacity limit: Let the transmission power of each transmission line j in the power grid be P j , and the maximum transmission power be P j，max . Then the power grid transmission capacity limit constraint is: P j ≤P j，max ; Power supply-demand balance: Let the power supply include the power generation of power generation resources P gen , the discharging amount of energy storage resources Q discharge , and the power import volume I import , and the power demand include the conventional power consumption load L, the charging demand of energy storage resources Q charge , and the power export volume I export , which is expressed as: P gen +Q discharge +I import = L + Q charge +I export .

[0045] Use the genetic algorithm to solve this optimization problem. Encode the trading plan variables (such as the discharging amount of each energy storage resource and the power generation of different power generation resources), randomly initialize the population, use the objective function related to the comprehensive efficiency as the fitness function, select individuals through the roulette wheel selection operation, perform crossover and mutation operations on the selected individuals, update the population after the operations, and judge whether the maximum iteration number T MAX, if the condition is met, output the trading plan corresponding to the individual with the highest fitness value in the current population as the optimal solution; if not, continue the iteration. The generated trading plan is sent to relevant entities (such as power generation enterprises, energy storage operators, and electricity retailers) in real time. Key data such as the actual charge and discharge power, energy, and trading execution time of energy storage resources are collected in real time through Internet of Things technology and intelligent metering devices, and compared with the expected values of the trading plan to calculate the execution deviation rate and execution progress indicators. The trading plan is dynamically adjusted based on the monitoring feedback information and market changes to ensure the effectiveness and adaptability of the trading plan.

[0046] Construct a multi-level regulatory framework for the electricity spot market that includes legal regulations (supervised according to relevant national and local electricity laws and regulations), administrative supervision (supervised by government energy management departments), industry self-discipline (industry associations formulate industry norms to guide enterprises to comply), and social supervision (encourage the public to supervise and report on electricity market behaviors), and formulate rules.

[0047] Establish a multi-dimensional performance evaluation index system that includes resource utilization efficiency (such as the utilization rate of energy storage resources and the utilization hours of power generation resources), market operation efficiency (such as the speed of transaction conclusion and transaction costs), power system stability (evaluated by monitoring grid frequency and voltage fluctuation indicators), and market participant satisfaction (collected through questionnaires or online evaluations).

[0048] Adopt an evaluation method that combines quantitative analysis (calculating index values based on specific data) and qualitative analysis (such as analyzing and summarizing the opinions and suggestions of market participants). Regularly (such as monthly or quarterly) conduct a comprehensive evaluation of the operation performance of the electricity spot market model. Based on the evaluation results, formulate targeted improvement suggestions and optimization measures. For example, if the market operation efficiency is found to be low, further simplify the trading process; if there are risks to the power system stability, strengthen the monitoring and control of grid operation, and continuously improve the operation efficiency and quality of the electricity spot market.

[0049] In summary, in the scenario of peak summer electricity consumption, through comprehensive energy storage information management, accurate power supply and demand forecasting, perfect trading platform construction, optimized trading plan guidance, and effective market supervision and evaluation, this electricity spot market model can make full use of energy storage resources, respond to power supply and demand changes in advance, ensure the stable operation of the power system, achieve the reasonable allocation of power resources and the comprehensive improvement of the benefits of all parties, effectively relieve the pressure of peak summer electricity consumption, and improve the operation efficiency and reliability of the electricity market.

[0050] Example 2:

[0051] Operation of the electricity spot market during winter heating

[0052] Collect the technical parameters of energy storage technologies applicable in winter in this region (such as electric boiler heat storage energy storage, phase change material energy storage), including the heat storage temperature range and the latent heat of phase change of the phase change material; geographical location information to clarify the distribution location of energy storage facilities; information on the affiliated operating entity, and monitor its operating status in real time, such as the current temperature of the heat storage equipment and the energy storage power, and construct an information database.

[0053] Collect historical winter electricity supply and demand data, analyze and obtain that the peak electricity demand for heating at night in winter is relatively high, while it is relatively low during the day and has a negative correlation with the temperature. Create an electricity supply and demand prediction model, and set the predicted electricity demand value as D p , the average value of electricity demand in the same historical period is The seasonal influence factor is S i , the time trend coefficient is T t , the influence value of sudden factors is U f , then the electricity demand prediction formula is: Predict that the electricity demand will increase significantly during the cold period in winter. Combining the prediction results, formulate a cross-season energy storage strategy. Use the surplus wind power and photovoltaic clean energy for energy storage during the low electricity price period in autumn, and give priority to the electric boiler heat storage energy storage technology with high heat storage efficiency and low energy storage cost. Conduct a comprehensive quantitative evaluation of different new energy storage resources through the energy storage resource evaluation and selection formula. Set the comprehensive evaluation index of energy storage resources as S, the energy storage capacity as D, the charge-discharge efficiency as η, the energy storage cost as K, the expected service life as L, the reliability factor as R, the market demand response flexibility factor as F, and the predicted electricity demand value as D p , the power supply capacity is P s , the importance factor of power supply and demand balance is B i , the formula is: Formulate a cross-season energy storage strategy in combination with the output results of the electricity supply and demand prediction model.

[0054] Collect winter meteorological data (such as temperature, snowfall), macroeconomic data (such as local winter tourism income, changes in commercial heating demand), and industrial project planning information (such as whether there is a new large-scale heating enterprise put into production). Analyze and find that for every 1°C decrease in temperature, the heating electricity demand increases by a certain value. The commercial heating demand increases significantly during the peak winter tourism season, and newly put into production heating enterprises will increase the electricity demand. Optimize the electricity supply and demand prediction model according to these influencing factors, and use the electricity supply and demand prediction correction formula. Set the original predicted electricity demand value as D p , the meteorological influence factor is W, the economic development influence factor is E, the industrial project change influence factor is I, the time trend correction factor is T, and the calculation formula is: D = D p ×(1 + W + E + I + T).

[0055] Set the warning threshold for supply and demand changes. Set the electricity demand warning threshold: Set the electricity demand warning threshold as Dth , the average of the highest daily electricity demand during the same historical period is Then the electricity demand warning threshold is: When the predicted electricity demand value D opt exceeds this threshold, an electricity demand warning signal is triggered. The electricity supply warning threshold is set as follows: For the power generation capacity, let the total power generation capacity of various power generation resources be P total , and the safety margin coefficient is α. Then the electricity supply warning threshold is: P th =(1 - α)×P total , when the predicted electricity demand exceeds the electricity supply warning threshold, it indicates that the electricity supply is facing a tense situation, triggering an electricity supply warning signal. Conduct a risk assessment of the warning of the supply and demand changes, determine that the growth of electricity demand may lead to challenges to the stability of the power system, fluctuations in the electricity market price, and tight electricity supply in some areas. According to the risk assessment results, adjust the subsequent trading plan, such as increasing the electricity generation and grid connection volume of clean energy, coordinating power support from surrounding areas, and at the same time formulating emergency measures, such as appropriately reducing the heating temperature in some non-critical heating areas and starting backup heating equipment.

[0056] Formulate cross-season electricity energy storage spot trading rules, and clarify the key rule terms such as the access conditions for energy storage resources to participate in the transaction (such as the safety performance of energy storage equipment and the heating quality standard), the trading process (the whole process specification from declaration to settlement), and the settlement method (settlement according to the actual heating electricity volume and negotiated price).

[0057] Design cross-season electricity energy storage spot trading contracts, covering the trading electricity volume (stipulating the electricity volume or heat that energy storage resources should provide during the winter heating period), price (determining a reasonable price based on costs and market supply and demand), time window (clarifying that energy storage resources must ensure the heating power supply during cold periods), the quality and technical standards of energy storage resources (such as heating stability and the thermal efficiency of heat storage equipment), and liability for breach of contract terms (regulations on restraining and punishing breach of contract behaviors).

[0058] During the peak season of winter heating electricity demand, the owners of energy storage resources and the demand sides (such as heating enterprises and residential community property management) prepare materials and declare transactions according to the rules and contracts. After being matched and signed by the trading platform, the transaction is executed. The trading platform monitors the energy storage dispatching in real time (ensuring that the heat storage equipment supplies heat according to the plan) and the power transmission (ensuring the safe and stable supply of electricity to the heating equipment), and conducts settlement and liquidation according to the agreement to ensure the smooth progress of the transaction.

[0059] With the goal of maximizing the overall operating efficiency of the power system, the comprehensive income of energy storage resources is I s 、the reliability benefit of electricity supply is I r 、the activity benefit of market trading is I a 、the environmental benefit is I eConstruct a mathematical model for trading plan optimization, assuming that the overall operating efficiency of the power system is U, and the constraint function set is G, including power supply and demand balance, energy storage capacity limitation, and power grid transmission limitation. The formula is: U = max(I s +I r +I a +I e ), where the specific content of the constraint function set G is: Energy storage capacity limit: For each energy storage resource point i, let its storage capacity be E i , the minimum storage capacity is E i，min , the maximum storage capacity is: E i，max , the energy storage capacity constraint is: E i，min ≤E i ≤E i，max At the same time, let the charging and discharging power of the energy storage resource, the charging power P i，charge ≤P i，charge，max , discharge power P i，discharge ≤P i，discharge，max ; Grid transmission capacity limitation: Assume that the transmission power of each transmission line j in the grid is P j , the maximum transmission power is P j，max , then the power grid transmission capacity constraint is: P j ≤P j，max ; Power supply and demand balance: Assume that the power supply includes the power generation of power generation resources P gen , the discharge amount of energy storage resources Q discharge and electricity imports I import , power demand includes conventional power load L, charging demand of energy storage resources Q charge And the electricity export volume I export , expressed as: P gen +Q discharge +I import =L+Q charge +I export .

[0060] Genetic algorithm is used to solve the optimization problem. The trading plan variables (such as the heating power of each energy storage resource and the power generation of different power generation resources) are encoded, the population is randomly initialized, and the comprehensive benefit objective function is used as the fitness function. Roulette selection, crossover and mutation operations are performed to update the population and determine whether the maximum number of iterations T is met. MAX If the conditions are met, the optimal trading plan will be output; if not, the iteration will continue and the trading plan will be issued to relevant entities. Actual data will be collected through Internet of Things technology and smart metering equipment, and compared and analyzed with expected values. The trading plan will be dynamically adjusted according to monitoring feedback and market changes to ensure stable and reliable power supply during the heating period.

[0061] Build a multi-level regulatory framework for the electricity spot trading market, including legal norms based on laws and regulations, administrative supervision by government departments, industry self-discipline promoted by industry associations, public participation in social supervision, and formulate corresponding rules. Establish a multi-dimensional effectiveness evaluation index system covering resource utilization efficiency (such as the utilization rate of energy storage resources for heating and the effective utilization of power generation resources), market operation efficiency (trading speed, cost), power system stability (judging by monitoring grid parameters), and market participant satisfaction (collecting opinions from all parties). Adopt a combined evaluation method of quantitative and qualitative, and comprehensively evaluate the operation effectiveness of the electricity spot trading market model regularly (such as monthly or quarterly). According to the evaluation results, formulate improvement suggestions and optimization measures. For example, if the resource utilization efficiency is low, optimize the allocation of energy storage resources; if the market operation efficiency is not high, improve the functions of the trading platform, and continuously improve the operation effect and service quality of the electricity spot trading market in the winter heating scenario.

[0062] In summary, during the winter heating period, this electricity spot trading market model operates collaboratively from various aspects such as energy storage information management, supply and demand forecasting, trading platform construction, trading optimization to market supervision and evaluation according to the characteristics of winter electricity supply and demand. Select and utilize energy storage technologies, accurately predict changes in supply and demand and give early warnings in a timely manner, standardize trading processes and contracts, optimize trading plans, comprehensively supervise and evaluate market effectiveness, ensure the stable and reliable supply of electricity for winter heating, take into account the benefits of all parties, promote the healthy and orderly development of the electricity market, and improve the overall operation level of the power model.

[0063] The above is only a preferred embodiment of the present invention, and does not impose any form of limitation on the present invention. Although the present invention has been disclosed above with the preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments by using the disclosed technical content within the scope of the technical solution of the present invention. However, as long as it does not depart from the content of the technical solution of the present invention, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for reporting quantity in a spot electricity trading market, characterized in that: The system includes: energy storage intelligent data collection module, power supply and demand forecasting module, trading rule building module, trading optimization and regulation module, and market supervision and model optimization module; The energy storage intelligent management acquisition module collects technical parameters, geographic location information, and information about the operating entity of compressed air energy storage and molten salt energy storage, monitors the operating status in real time to build an information database, collects historical power supply and demand data, analyzes the inherent laws of power supply and demand in different seasons and time periods to create a power supply and demand forecasting model, and formulates a cross-season energy storage strategy based on the output results of the power supply and demand forecasting model; The power supply and demand forecasting module: collects data on meteorological data, macroeconomic data and industrial project planning information, analyzes their impact on power supply and demand data, optimizes the power supply and demand forecasting model, and sets a supply and demand change warning threshold based on the forecast results of the optimized power supply and demand forecasting model. When it is predicted that the power supply and demand situation will change significantly in a certain season or time period and exceeds the set threshold, a warning signal is triggered, and a risk assessment is conducted on the supply and demand changes warned, and the impact on the stability of the power system, power market price fluctuations and power supply is analyzed. According to the risk assessment results, subsequent trading plans are adjusted and emergency measures are initiated; The trading rules building module: formulate cross-seasonal power storage spot trading rules, clarify the entry conditions, trading process, and settlement method rules for energy storage resources to participate in the transaction, and design cross-seasonal power storage spot trading contracts that cover trading volume, price, time window, energy storage resource quality technical standards, and breach of contract liability clauses. During the peak season of electricity demand, energy storage resource owners and demanders prepare and declare according to the rules and contracts. After the trading platform matches and signs the contract, the transaction is executed and the energy storage dispatch and power transmission are monitored, and settlement is made as agreed. The transaction optimization and control module: with the goal of maximizing the overall operational benefits of the power system, comprehensively considers the benefits of energy storage resources, the benefits of power supply reliability, the benefits of market transaction activity, and the environmental benefits to construct a mathematical model for optimizing the transaction plan, sets the power supply and demand balance, energy storage capacity limit, and power grid transmission capacity limit as constraints, uses genetic algorithms to solve, and issues the generated transaction plan to relevant entities in real time. Through the Internet of Things technology and smart metering equipment, the data of actual charge and discharge of energy storage resources, power, and transaction execution time are collected in real time, compared with the expected values ​​of the transaction plan, and the execution deviation rate and execution progress indicators are calculated, and the transaction plan is dynamically adjusted according to monitoring feedback information and market changes; The market supervision and model optimization module: constructs a multi-level electricity spot trading market supervision framework covering legal norms, administrative supervision, industry self-discipline and social supervision and formulates rules, establishes a multi-dimensional performance evaluation index system including resource utilization efficiency, market operation efficiency, power system stability and market participant satisfaction, regularly conducts a comprehensive evaluation of the operation efficiency of the electricity spot trading market model using an evaluation method that combines quantitative analysis with qualitative analysis, and formulates targeted improvement suggestions and optimization measures based on the evaluation results.

2. A method for reporting quantity in a spot power trading market according to claim 1, characterized in that: In the energy storage intelligent data collection module, a power supply and demand prediction model is created based on historical power supply and demand data through a comprehensive power demand prediction formula. The power demand prediction value is set to D p The average electricity demand during the same period in history was The seasonal impact factor is S i , the time trend coefficient is T t , the impact value of sudden factors is U f , then the power demand forecast formula is:

3. A method for reporting quantity in a spot power trading market according to claim 1, characterized in that: In the energy storage intelligent collection module, a comprehensive quantitative evaluation of different new energy storage resources is carried out through the energy storage resource evaluation and selection formula to formulate a cross-season energy storage strategy. The comprehensive evaluation index of energy storage resources is S, the energy storage capacity is D, the charging and discharging efficiency is η, the energy storage cost is K, the expected service life is L, the reliability factor is R, the market demand response flexibility factor is F, and the power demand forecast value is D. p , the power supply capacity is P s , the importance factor of power supply and demand balance is B i , the formula is:

4. A method for reporting quantity in a spot power trading market according to claim 1, characterized in that: The power supply and demand forecast module uses the power supply and demand forecast correction formula to perform multi-factor correction on the original power demand forecast value. Suppose the original power demand forecast value is D p , the meteorological impact factor is W, the economic development impact factor is E, the industrial project change impact factor is I, and the time trend correction factor is T. The calculation formula is: D = D p ×(1+W+E+I+T).

5. The method for reporting quantity in the spot power trading market according to claim 1, characterized in that: The supply and demand change warning threshold in the power supply and demand forecasting module: The power demand warning threshold setting: Set the power demand warning threshold as D th The average daily maximum electricity demand during the same period in history was The power demand warning threshold is: When the predicted power demand value D opt When this threshold is exceeded, an early warning signal for power demand is triggered; The power supply warning threshold is set as follows: For power generation capacity, the total power generation capacity of various power generation resources is set as P total , the safety margin coefficient is α, then the power supply warning threshold is: P th =(1-α)×P total ,When the predicted power demand exceeds the power supply warning threshold, the power supply warning signal is triggered.

6. A method for reporting quantity in a spot power trading market according to claim 1, characterized in that: Emergency measures in the power supply and demand forecasting module: The emergency measures for power demand side management are as follows: according to the pre-established rotation plan, some industrial enterprises are arranged to stop production and give up power during peak hours; Implement staggered operation management for air conditioning and lighting equipment in commercial places; implement tiered electricity price adjustments or time-of-use electricity price preferential policies for residential users to encourage residents to reduce electricity consumption during peak hours and ease power supply pressure; The emergency measures on the power supply side include: starting the backup generator sets and optimizing the scheduling of power generation resources to ensure the power supply to hospitals, communication hubs, transportation hubs, urban centers, and industrial parks.

7. A method for reporting quantity in a spot power trading market according to claim 1, characterized in that: The trading plan optimization mathematical model constructed in the trading optimization and regulation module assumes that the overall operation benefit of the power system is U and the energy storage resource benefit is I s , the power supply reliability benefit is I r , the market transaction activity benefit is I a , environmental benefit is I e The constraint function set is G, which includes power supply and demand balance, energy storage capacity limitation, and grid transmission limitation. The formula is: U = max(I s +I r +I a +I e ); StG.

8. A method for reporting quantity in a spot power trading market according to claim 7, characterized in that: The specific content of the constraint function set G in the transaction optimization and regulation module is: Energy storage capacity limit: For each energy storage resource point i, let its storage capacity be E i , the minimum storage capacity is E i，min , the maximum storage capacity is: E i，max , the energy storage capacity constraint is: E i，min ≤E i ≤E i，max At the same time, let the charging and discharging power of the energy storage resource, the charging power P i，charge ≤P i，charge，max , discharge power P i，discharge ≤P i，discharge，max ; Grid transmission capacity limitation: Assume that the transmission power of each transmission line j in the grid is P j , the maximum transmission power is P j，max , then the power grid transmission capacity constraint is: P j ≤P j，max ; Electricity supply and demand balance: Assume that the electricity supply includes the power generation of the power generation resources P gen , the discharge amount of energy storage resources Q discharge and electricity imports I import , power demand includes conventional power load L, charging demand of energy storage resources Q charge And the electricity export volume I export , expressed as: P gen +Q discharge +I import =L+Q charge +I export .

9. The method for reporting quantity in the spot power trading market according to claim 1, characterized in that: The steps of using genetic algorithm to solve the trading plan optimization problem of comprehensive multi-benefit construction in the trading optimization and regulation module are as follows: encoding the trading plan variables, randomly initializing the population, taking the objective function related to the comprehensive benefit as the fitness function, selecting individuals through roulette selection operation, performing crossover and mutation operations on the selected individuals, and updating the population after the operation to determine whether the maximum number of iterations T is met. MAX , the iteration is terminated when the maximum number of iterations is reached, and the trading plan corresponding to the individual with the highest fitness value in the current population is output as the optimal solution. If it is not satisfied, continue the above crossover and mutation operations.

Images