New energy electrothermal coupling cooperative participation power operation and transaction strategy optimization method

Abstract

The invention discloses a new energy electric-thermal coupling cooperative participation power operation and transaction strategy optimization method, and the method comprises the steps: constructing an electric-thermal coupling system cooperative operation frame integrating wind power, photovoltaic power, an electric boiler, a heat storage device and electrochemical energy storage, and defining the operation constraint of each device; then collecting multi-dimensional data of electricity market, energy supply and demand and equipment state, and predicting related data in a future time period; then, a collaborative transaction strategy optimization model which takes system comprehensive income maximization as a target and comprises multiple markets and heat supply income is constructed; then, an intelligent optimization algorithm is combined with equipment constraint and prediction data for solving, and the multi-market capacity and heat supply capacity participated by the system in each time period are determined; and finally, generating a scheduling strategy according to a solving result to control equipment operation. According to the method, multi-device cooperation and multi-market participation are realized, the new energy consumption capability, the system operation efficiency and the comprehensive benefit are improved, and a scientific strategy is provided for operation and transaction of the electrothermal coupling system.

Description

Technical Field

[0001] This invention relates to the field of power operation and trading technology, and in particular to a method for optimizing power operation and trading strategies involving the synergistic participation of new energy sources in electrothermal coupling. Background Technology

[0002] Currently, the proportion of new energy sources such as wind power and photovoltaics in the energy system continues to increase, but their output exhibits significant fluctuations and intermittency, posing challenges to the stable operation of the power system. Simultaneously, the demand for electricity and heat loads is showing a diversified growth trend. Under the traditional energy supply model, electricity and heat systems operate independently, making it difficult to achieve efficient energy utilization. With the advancement of electricity market reforms and the gradual improvement of multi-market trading systems such as spot markets, frequency regulation markets, and reserve markets, the demand for new energy power plants and related energy equipment to participate in electricity market transactions is becoming increasingly urgent. How to achieve new energy consumption, improve system operating efficiency, and maximize market returns through electro-thermal coupling and synergy has become a key issue that urgently needs to be addressed in the energy sector, and has also promoted the research and application of optimization methods for new energy electro-thermal coupling and synergy participation in power operation and trading strategies.

[0003] Existing technologies have significant shortcomings in the synergistic participation of new energy power and heat in power operation and trading. On the one hand, there is insufficient synergy. Most technical solutions are designed only for a single energy form or a single market trading scenario, failing to fully integrate resources from multiple devices such as wind power, photovoltaics, electric boilers, thermal storage devices, and electrochemical energy storage. This results in a lack of comprehensive framework for the coordinated operation of the electric and thermal coupling system, leading to low coordination between devices and difficulty in adapting to the complex operational needs of multi-market trading environments. On the other hand, there is a lack of optimization dimensions. Existing optimization models often focus on a single revenue objective or a single operational constraint, failing to comprehensively consider the revenue composition of multiple markets such as spot market, frequency regulation, and reserve, and also failing to fully incorporate the operational constraints of various devices. This makes it difficult for the optimization results to balance the overall system revenue and operational stability, and thus cannot provide scientific and comprehensive operational and trading strategy support for new energy power and heat coupling systems. Summary of the Invention In order to overcome the shortcomings and deficiencies of existing technologies, this invention provides a method for optimizing power operation and trading strategies by coordinating the electrothermal coupling of new energy sources.

[0004] The technical solution adopted in this invention is a method for optimizing power operation and trading strategies through the coordinated participation of new energy electrothermal coupling, comprising the following steps: S1, constructing a coordinated operation framework for the electrothermal coupling system, integrating wind power, photovoltaics, electric boilers, thermal storage devices, and electrochemical energy storage, and establishing models for each equipment component. Specifically, the wind power output model is divided into four output states based on wind speed: below the cut-in wind speed, between the cut-in and rated wind speed, between the rated and cut-out wind speed, and at the cut-out wind speed. The photovoltaic output model is constructed by combining solar irradiance, ambient temperature, and component efficiency. Simultaneously, the operational constraints of each device are defined: wind power constraints include upper and lower limits of output and ramp-up restrictions; photovoltaic constraints include upper and lower limits of output; electric boiler constraints include power range and start-stop interval restrictions; thermal storage device constraints include capacity, charging / discharging power, and temperature restrictions; and electrochemical energy storage constraints include the state of charge range and charging / discharging power restrictions. S2, collecting multi-dimensional operational data, including spot market data. The system comprises six phases: S1, S2, S3, and S4. S4. S5. S6. S7. S8. S9. S1. S2. S3. S4. S5. S6. S7. S8. S9. S1. S8. S9. S1. S2. S3. S8. S9. S1 ...1. S9. S1. S1. S9. S1. S1. S9. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1. S1

[0005] Furthermore, the total revenue calculation model for the spot market in S4 is as follows: ,in, This represents the total revenue in the spot market. Indicates the trading session number for the next day. This indicates the total number of trading sessions within the next day. Indicates the first Spot electricity price for the period Indicates the first The trading volume of the time period system participating in the spot market.

[0006] Furthermore, the calculation model for the total revenue of the frequency modulation market in S4 is as follows: ,in, This represents the total revenue of the FM market. This indicates the frequency modulation capacity that the system will bid for in the next day. This indicates the winning bid price in the frequency modulation capacity market. This indicates the sequence number of the frequency modulation response period within the next day. This indicates the total number of frequency modulation response periods within the next day. Indicates the first Frequency modulation mileage during the time period Indicates the first Market price for frequency modulation mileage during specific time periods.

[0007] Furthermore, the wind power output model in S1 is as follows: ,in, Indicates the first Wind power output during certain periods Indicates the first Wind speed during the period, This indicates the wind speed at which the fan cuts in. This indicates the fan's output air velocity. Indicates the rated wind speed of the fan. This indicates the rated output of the fan.

[0008] Furthermore, the photovoltaic output model in S1 is as follows: ,in, Indicates the first Solar power output during specific time periods Indicates the conversion efficiency of photovoltaic modules. Indicates the first Solar irradiance during the time period Indicates the area of ​​the photovoltaic array. Indicates the temperature coefficient of photovoltaic modules. Indicates the first ambient temperature during the period This indicates the efficiency of photovoltaic modules under standard test conditions. Indicates solar irradiance under standard test conditions. This indicates the ambient temperature under standard test conditions.

[0009] Furthermore, the heat storage capacity variation model of the thermal storage device in S1 is as follows: ,in, Indicates the first The time-slot thermal storage device stores heat in real time. Indicates the first The time-slot thermal storage device stores heat in real time. Indicates the first The charging power of the time-limited thermal storage device Indicates time interval, This indicates the heat charging efficiency of the thermal storage device. Indicates the first Heat release power of time-limited thermal storage device This indicates the heat release efficiency of the thermal storage device.

[0010] Further, S2 includes the following sub-steps: S21, obtaining spot electricity price data for each trading period within the next day through the electricity market trading platform interface, and simultaneously collecting demand signals released by the frequency regulation and standby ancillary service markets, including the required service capacity and corresponding revenue standards, to establish an electricity market data ledger; S22, using sensors deployed in wind farms and photovoltaic power stations to collect wind power and photovoltaic output data in real time, with a sampling interval set to 15 minutes, and simultaneously collecting heat load demand and electricity load demand data through the user-side energy management system, recording the peak and valley values ​​of demand for each load period; S23, obtaining the current thermal storage capacity of the thermal storage device through the equipment status monitoring module, reading the remaining power and state of charge of the electrochemical energy storage through the energy storage management system, and collecting its operating parameters through the electric boiler control system, including current operating power, number of start-stop cycles, and cumulative operating time, to ensure the completeness and real-time nature of the data collection for each device status.

[0011] Further, S3 includes the following sub-steps: S31, based on the historical wind power output data and historical wind speed data collected in S2, a wind power output prediction model is established using time series analysis, and the wind speed prediction data for the next day is input to obtain the predicted wind power output values ​​for each time period; S32, combining the historical photovoltaic output data, solar irradiance, and ambient temperature data collected in S2, a photovoltaic output prediction model is constructed using machine learning algorithms, and the solar irradiance and ambient temperature prediction data for the next day released by the meteorological department are imported to generate the predicted photovoltaic output values ​​for each time period; S33, based on the historical heat load and electricity load demand data obtained in S2, considering seasonal, holiday, and user energy consumption habits, a regression analysis method is used to establish heat load and electricity load demand prediction models respectively, and the predicted heat load and electricity load demand values ​​for each time period in the next day are output; S34, referring to the historical electricity price data and historical demand and price data of ancillary services in the electricity market, combined with macroeconomic factors, energy policies, and grid operation status, a trend prediction method is used to predict the spot electricity price, frequency regulation and reserve service demand, and corresponding prices for each trading period in the next day.

[0012] Further, S4 includes the following sub-steps: S41, clarifying the composition of the system's comprehensive revenue, incorporating spot market revenue, frequency regulation market revenue, reserve market revenue, and heating revenue into the objective function, determining the weight of each revenue item in the total revenue, and ensuring that the objective function can comprehensively reflect the system's revenue situation; S42, for spot market revenue, constructing a sub-model for calculating spot market revenue based on the predicted spot electricity price for each time period of the next day and the range of tradable electricity in the system, clarifying the correlation between tradable electricity and electricity price; S43, for frequency regulation market revenue, establishing a sub-model for calculating frequency regulation market revenue by combining the predicted value of the system's bid-winning frequency regulation capacity, the predicted value of the winning bid price in the frequency regulation capacity market, and the predicted value of the frequency regulation mileage and the predicted value of the frequency regulation mileage market price for each time period; S44, constructing a sub-model for calculating reserve market revenue based on the predicted value of reserve capacity demand and the predicted value of reserve capacity price for each time period in the reserve market; S45, establishing a sub-model for calculating heating revenue based on the predicted value of heat load demand and the predicted value of heating price for each time period of the next day, and finally integrating the sub-models to form a complete objective function for the collaborative trading strategy optimization model.

[0013] Further, S5 includes the following sub-steps: S51, defining variables in the collaborative trading strategy optimization model constructed in S4, clarifying that the decision variables are the system's participation in the spot, frequency regulation, and reserve markets, and determining the value range of each decision variable; S52, transforming the equipment operation constraints established in S1 into mathematical constraints, including upper and lower limits for wind power output, upper and lower limits for photovoltaic power output, power and start-up / shutdown constraints for electric boilers, capacity, charging / discharging power and temperature constraints for thermal storage devices, and state of charge and charging / discharging power constraints for electrochemical energy storage, ensuring the constraints are met. Accuracy and executability; S53, select the intelligent optimization algorithm, set the algorithm's iteration count, population size, and convergence accuracy parameters, and input the prediction data obtained in S3 and the constraints established in S52 into the model; S54, run the intelligent optimization algorithm to solve the model, monitor the algorithm's convergence in real time during the solution process, and adjust the algorithm parameters if slow convergence or getting stuck in a local optimum is observed until the optimal solution that satisfies the constraints and maximizes the system's overall benefit is obtained, and determine the system's capacity and heat supply for each time period in the next day in the spot, frequency regulation, and reserve markets.

[0014] Compared with the prior art, the present invention has at least one of the following beneficial effects: This invention proposes a method for optimizing the strategy of electric and thermal coupling of new energy sources in power operation and trading. By constructing a collaborative operation framework for an electric and thermal coupling system integrating wind power, photovoltaics, electric boilers, thermal storage devices, and electrochemical energy storage, and combining multi-dimensional operation data collection and future time period data prediction, it achieves comprehensive coordination of multi-device resources and precise control of multi-market trading data, effectively improving the capacity for new energy absorption and system operation efficiency. Furthermore, by constructing a collaborative trading strategy optimization model with the goal of maximizing the overall system benefits, including spot market, frequency regulation, reserve market, and heating revenue, and solving it with intelligent optimization algorithms, it clarifies the capacity and heat supply of the system participating in multiple markets at each time period, helping the system obtain higher overall benefits. Meanwhile, this method solves the problems of insufficient synergy, low equipment compatibility, and difficulty in adapting to the complex needs of multiple markets by comprehensively integrating multi-device resources and building a complete electric and thermal coupling collaborative operation framework. By comprehensively considering the revenue composition of multiple markets and the operational constraints of various devices, it constructs a multi-dimensional optimization model to solve the problem of traditional optimization having a single dimension and being unable to take into account the comprehensive system revenue and operational stability. This provides scientific and comprehensive operation and trading strategy support for new energy electric and thermal coupling systems. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the overall method steps of the present invention; Figure 2 This is a flowchart of method step S2 of the present invention; Figure 3 This is a flowchart of method step S3 of the present invention; Figure 4 This is a flowchart of method step S4 of the present invention; Figure 5 This is a flowchart of step S5 of the method of the present invention. Detailed Implementation

[0016] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0017] like Figure 1 As shown, the method for optimizing power operation and trading strategies through the synergistic participation of new energy sources in power generation and thermal coupling includes the following steps: S1. Construct a collaborative operation framework for the electric and thermal coupling system, integrating wind power, photovoltaics, electric boilers, thermal storage devices, and electrochemical energy storage. Establish models for each equipment component. Among them, the wind power output model is divided into four output states based on wind speed: below the cut-in wind speed, between the cut-in and rated wind speed, between the rated and cut-out wind speed, and cut-out wind speed. The photovoltaic output model is constructed by combining solar irradiance, ambient temperature, and component efficiency. At the same time, the operating constraints of each device are clarified. The constraints for wind power include upper and lower limits of output and ramping restrictions. The constraints for photovoltaics include upper and lower limits of output. The constraints for electric boilers include power range and start-stop interval restrictions. The constraints for thermal storage devices include capacity, charging and discharging power, and temperature restrictions. The constraints for electrochemical energy storage include the state of charge range and charging and discharging power restrictions. Specifically, step S1 aims to construct a collaborative operation framework for the electric and thermal coupling system and clarify equipment constraints, laying the foundation for subsequent optimization. During implementation, wind power, photovoltaics, electric boilers, thermal storage devices, and electrochemical energy storage are integrated. Wind power units are selected with a cut-in wind speed of 3 m / s, a cut-out wind speed of 25 m / s, and a rated wind speed of 13 m / s. Photovoltaic units use monocrystalline silicon modules with a conversion efficiency of 22% and a temperature coefficient of -0.38% / ℃. The electric boiler is a resistance type with a rated power of 10 MW, a minimum power of 3 MW, and a start-stop interval of 30 minutes. The thermal storage device has a capacity of 50 MWh, a maximum charge / discharge power of 8 MW, and a temperature range of 80℃-120℃. The electrochemical energy storage has a rated capacity of 20 MWh, a state of charge of 20%-90%, and a maximum charge / discharge power of 10 MW. Next, equipment models were established. Wind power was categorized into four output states based on wind speed, while photovoltaic power output was calculated based on irradiance, temperature, and component parameters. Constraints were defined: wind power ramp rate should not exceed 5% / minute of rated power; electric boiler power should be 3-10 MW with a start-stop interval of ≥30 minutes; thermal storage devices should have a heat storage capacity of 10-50 MWh and a charge / discharge power of ≤8 MW; and electrochemical energy storage should have a state of charge of 20%-90% and a charge / discharge power of ≤10 MW. These parameters and models were used to construct a complete operational framework, providing a foundation for subsequent data collection and optimization.

[0018] S2 collects multi-dimensional operational data, including spot electricity prices in the electricity market, ancillary service demand and revenue standards, real-time output of wind and solar power, heat load and electricity load demand in energy supply and demand, and equipment status such as thermal storage capacity, remaining energy storage capacity and electric boiler operating conditions. Specifically, step S2 is responsible for multi-dimensional operational data collection, providing data support for subsequent prediction and optimization, and ensuring data integrity and real-time performance. Electricity market data is obtained through API integration with the regional trading platform, collecting spot electricity prices every 15 minutes for a total of 96 time periods per day. Simultaneously, it collects frequency regulation demand (capacity not less than 2% of the region's maximum load per time period, capacity price 200 yuan / MWh, mileage price 150 yuan / MWh) and reserve demand (capacity not less than 5% of the region's maximum load per time period, price 180 yuan / MWh), with a collection frequency of 15 minutes per time. In energy supply and demand data, wind power output is collected every 5 minutes using ultrasonic anemometers and power sensors, with an accuracy of 0.1 MW; photovoltaic data (irradiance accuracy 1 W / m², temperature accuracy 0.1℃, output accuracy 0.1 MW) is collected every 5 minutes; electricity and heat load demand are collected every 15 minutes using smart meters, with accuracies of 0.01 MW and 0.01 MWh, respectively, recording peak and valley periods and values. In the equipment status data, the remaining energy of the thermal storage device (accuracy 0.1 MWh) and temperature (accuracy 0.1℃) are collected every 10 minutes; the remaining energy of the electrochemical energy storage (accuracy 0.1 MWh) and state of charge (accuracy 1%) are read every 5 minutes; and the power of the electric boiler (accuracy 0.1 MW), number of start-stop cycles (accuracy 1 time), and running time (accuracy 1 minute) are collected every 10 minutes. The data is stored locally and verified to remove outliers.

[0019] S3 predicts relevant data for future periods based on collected data, including spot electricity prices, frequency regulation and standby service demand and prices, wind and solar power output, heat load and electricity load demand for each trading period in the next day; Specifically, step S3 predicts key data for future periods based on the S2 data, providing forward-looking input for model optimization. The prediction period is 1 day, with each period lasting 15 minutes (a total of 96 periods). For wind power prediction, historical data collected in S2 over the past 3 months (no fewer than 17,280 sets) is selected, and modeling is performed using the ARIMA method. The training iterations are capped at 1000, with a convergence accuracy of 0.001. The meteorological department's 15-minute wind speed predictions (accuracy 0.1 m / s) are input, and the output power prediction value is output with an error within ±8%. For photovoltaic prediction, S2 data from the past 3 months (no fewer than 17,280 sets) is selected, and modeling is performed using the random forest algorithm with 100 decision trees and a maximum depth of 15 layers. The meteorological department's 15-minute irradiance (accuracy 1 watt / m²) and temperature (accuracy 0.1℃) predictions are imported to generate the output power prediction value with an error within ±10%. For heat and electricity load forecasting, nearly 6 months of S2 data (no fewer than 34,560 sets, including seasonal and holiday data) are collected. Multiple linear regression modeling is used, incorporating date type, seasonal coefficients (spring 0.9, summer 1.2, autumn 0.8, winter 1.5), and temperature factors. Future date and temperature forecasts are input, and load forecasts are output, with errors within ±7% and ±5%, respectively. For electricity market forecasting, nearly 6 months of S2 data (no fewer than 34,560 sets) are collected. Combining macroeconomic factors, energy policies, and grid conditions, a combination of grey forecasting and trend extrapolation is used to forecast spot electricity prices (error ±6%), frequency regulation and reserve demand and prices (error ±5%, ±8%). The results are organized into a structured dataset by time period.

[0020] S4. Construct a collaborative trading strategy optimization model with the goal of maximizing the overall system revenue. The objective function includes the spot market, frequency regulation market, reserve market, and heating revenue. Specifically, step S4 constructs a collaborative transaction optimization model with the goal of maximizing the overall system revenue, clarifying the objective function and its constraints. The objective function includes spot market revenue, frequency regulation revenue, reserve revenue, and heating revenue. Spot market revenue is the sum of the products of electricity price and transaction volume for each time period, with the transaction volume determined by considering the output of new energy sources and the equipment's adjustment capacity. Frequency regulation revenue is divided into capacity (bid-in capacity × capacity price) and mileage (mileage × mileage price for each time period) revenue, with the capacity not exceeding the sum of the adjustable capacities of energy storage and electric boilers, and the mileage calculated based on frequency deviation and response speed. Reserve revenue is the sum of bid-in capacity × reserve price for each time period, with the capacity meeting equipment constraints. Heating revenue is the sum of heat supply × heating price for each time period, with the heat supply matching the heat load and not exceeding the equipment's heating capacity. The weights of each revenue item are set according to market conditions: spot market 0.4, frequency regulation 0.25, reserve 0.2, and heating 0.15. Total revenue = 0.4 × spot market revenue + 0.25 × frequency regulation revenue + 0.2 × reserve revenue + 0.15 × heating revenue. Constraints are integrated for S1 equipment: wind power output is within ±8% of the predicted value and the ramp rate is ≤5% / minute; photovoltaic output is within ±10% of the predicted value; electric boiler power is 3-10 MW and the start-stop interval is ≥30 minutes; thermal storage device has a heat storage capacity of 10-50 MWh, a charge / discharge power of ≤8 MW, and a temperature of 80℃-120℃; electrochemical energy storage has a state of charge of 20%-90% and a charge / discharge power of ≤10 MW. In addition, energy balance constraints are added (electricity side: new energy output + energy storage discharge + electric boiler electricity consumption = electricity load + energy storage charging + electric boiler electricity consumption + spot trading; heat side: heat supply = heat load + difference between thermal storage charge / discharge), forming a complete optimization model.

[0021] S5 uses intelligent optimization algorithms to solve the model, and combines the equipment constraints in S1 and the prediction data in S3 to determine the system's capacity and heat supply for the spot, frequency regulation and standby markets in each time period of the next day. Specifically, step S5 uses an intelligent optimization algorithm to solve model S4, determining the multi-market participation capacity and heating supply for future periods, balancing accuracy and efficiency. The algorithm chosen is an improved particle swarm optimization algorithm, introducing dynamic adjustment of inertia weights (initially 0.9, linearly reduced to 0.4) and crossover / mutation (crossover probability 0.7, mutation probability 0.05) to avoid local optima. Parameters are set as follows: population size 200 (due to 384 decision variables: spot, frequency regulation, reserve, and heating data for 96 time periods), number of iterations 500, and convergence accuracy 10^-6 (optimal fitness change < 10 after 20 consecutive iterations). -6(Then convergence). Before solving, the S3 prediction data and S1 constraints are converted into the algorithm input format. The decision variable range is: spot trading -10 MWh (purchase) to 20 MWh (sale), frequency regulation 0-10 MWh, reserve 0-15 MWh, and heating 0-8 MWh. During the solution process, the population is initialized first (each particle is a set of decision variables), the fitness value (system comprehensive benefit) is calculated, and the global and individual optimal particles are updated. The velocity is adjusted and the position is updated according to the velocity formula (velocity = inertia weight × forward velocity + 2 × random number × (individual optimal - current position) + 2 × random number × (global optimal - current position)). If the range is exceeded, the process is truncated. Convergence is monitored during iteration. If convergence is not achieved after 300 iterations, the crossover probability is increased to 0.8 and the mutation probability is increased to 0.08 until convergence is achieved. The 96-period spot trading volume, frequency regulation capacity, reserve capacity, and heating capacity corresponding to the global optimal particle are output.

[0022] S6 generates an electric and thermal coordinated scheduling strategy based on the solution results, controls the operating status of each device, and enables the system to operate and trade in the electricity market.

[0023] Specifically, step S6 generates a coordinated power and heat dispatch strategy based on the results of S5, controlling equipment operation to achieve efficient market participation and ensuring accurate execution of instructions. During strategy generation, the decision variables for each time period output by S5 are converted into equipment instructions and organized into a dispatch table, clearly defining the wind and solar power output control targets (predicted value ± allowable fluctuation) every 15 minutes, the power of electric boilers (3-10 MW), the charging and discharging status and power of thermal storage devices, the charging and discharging status and power of electrochemical energy storage, the direction and amount of spot trading, and the frequency regulation and standby bidding capacity and response status. The strategy sends updates to the control systems of each device via industrial Ethernet every 15 minutes: wind turbines adjust pitch angle to control output (accuracy ±0.1 MW); photovoltaic systems adjust inverter power (accuracy ±0.05 MW); electric boilers adjust the number of heating tubes to control power (accuracy ±0.1 MW); thermal storage devices adjust circulating pump speed to control charging and discharging (accuracy ±0.05 MW) and monitor temperature (80℃-120℃); electrochemical energy storage devices adjust bidirectional converter charging and discharging (accuracy ±0.05 MW) and monitor charge status (20%-90%). The trading terminal generates orders according to the instructions and submits them to the spot market. Operation monitoring collects equipment parameters and trading data every 5 minutes, compares them with target values, and if the deviation exceeds the allowable range (output ±5%, power ±3%, trading ±10%), analysis is performed: equipment failure is initiated and backup is activated; predicted deviations are fine-tuned using real-time data for the next time period to ensure the system operates according to the optimal strategy and achieves market operation and trading objectives.

[0024] Preferably, the total revenue calculation model in the spot market in S4 is as follows: ,in, This represents the total revenue in the spot market. Indicates the trading session number for the next day. This indicates the total number of trading sessions within the next day. Indicates the first Spot electricity price for the period Indicates the first The trading volume of the time period system participating in the spot market.

[0025] Specifically, the total revenue calculation model for the spot market uses a parameter whose value depends on the sum of the products of electricity price and trading volume for all trading periods within a future day. This parameter has no fixed value and fluctuates with market conditions. The trading period number within a future day ranges from 1 to 96, corresponding to a 15-minute interval throughout the day. The total number of trading periods is fixed at 96, consistent with the maximum value of the period number, ensuring coverage of all trading nodes throughout the day. The spot electricity price for period t is obtained by collecting data from the regional power trading platform in step S2. This price typically ranges from 0.3 yuan / kWh to 1.2 yuan / kWh, with the specific value varying depending on supply and demand; for example, electricity prices are higher during peak hours and lower during off-peak hours. The electricity volume traded in the spot market during time period t is one of the decision variables, with a value ranging from -10 MWh to 20 MWh. A negative value represents the system purchasing electricity from the spot market, while a positive value represents the system selling electricity to the spot market. The specific value needs to be determined by combining the new energy output and load demand predicted in step S3 and solving the algorithm in step S5. In implementation, the electricity price and trading volume for each time period are first obtained or calculated according to the above parameter value rules. Then, the two for the corresponding time period are multiplied together. Finally, the product results of all 96 time periods are summed to obtain the total spot market revenue. This revenue value will be an important component of the objective function of the collaborative trading strategy optimization model in step S4 and will participate in the calculation of the system's overall revenue.

[0026] Preferably, the calculation model for the total revenue of the frequency modulation market in S4 is as follows: ,in, This represents the total revenue of the FM market. This indicates the frequency modulation capacity that the system will bid for in the next day. This indicates the winning bid price in the frequency modulation capacity market. This indicates the sequence number of the frequency modulation response period within the next day. This indicates the total number of frequency modulation response periods within the next day. Indicates the first Frequency modulation mileage during the time period Indicates the first Market price for frequency modulation mileage during specific time periods.

[0027] Specifically, the total revenue calculation model for the frequency regulation market involves adding the capacity revenue and mileage revenue. The total revenue parameter is not fixed and is influenced by frequency regulation market rules and system response capabilities. The frequency regulation capacity won in the next day, used as a decision variable, ranges from 0 to 10 MW. The specific value needs to be determined based on the frequency regulation market demand predicted in step S3, combined with the adjustable capacity of electrochemical energy storage and electric boilers within the system. If the predicted frequency regulation demand is high for a certain period, the won bid capacity can be appropriately increased to obtain more revenue. The winning bid price for the frequency regulation capacity market is obtained by collecting the revenue standards published by the regional frequency regulation market in step S2. The price is typically between 200 RMB / MWh and 500 RMB / MWh, varying depending on the season or electricity consumption period; for example, the price may rise during periods of high electricity demand in winter. The frequency regulation response period number for the next day ranges from 1 to 96, consistent with the trading period division in the spot market, ensuring time synchronization. The total number of frequency regulation response periods is fixed at 96, covering all periods throughout the day that may require frequency regulation response. The frequency regulation mileage for time period t is calculated based on the real-time frequency deviation signal of the power grid and the system's frequency regulation response speed, with a value ranging from 0 to 5 MWh. The larger the frequency deviation and the faster the response speed, the larger the frequency regulation mileage for that time period. The market price of the frequency regulation mileage for time period t is also obtained through step S2, and its value is usually between 150 yuan / MWh and 300 yuan / MWh, consistent with the trend of frequency regulation capacity market price changes. In implementation, the capacity revenue is first calculated (winning frequency regulation capacity multiplied by capacity price), then the mileage revenue for each time period is calculated (corresponding time period frequency regulation mileage multiplied by mileage price) and summed. Finally, the two are added together to obtain the total frequency regulation market revenue. This revenue is included in the objective function of step S4 to help maximize the overall system revenue.

[0028] Preferably, the wind power output model in S1 is: ,in, Indicates the first Wind power output during certain periods Indicates the first Wind speed during the period, This indicates the wind speed at which the fan cuts in. This indicates the fan's output air velocity. Indicates the rated wind speed of the fan. This indicates the rated output of the fan.

[0029] Specifically, in the wind power output model, the parameters of the wind power output in time period t vary with wind speed and fall into four categories. There are no fixed values, and the maximum value does not exceed the rated output of the wind turbine. The wind speed in time period t is collected by an ultrasonic anemometer deployed at the wind farm in step S2. The value ranges from 0 to 30 m / s, the collection frequency is once every 5 minutes, and the data accuracy is 0.1 m / s, providing real-time input for subsequent output calculations. The wind turbine cut-in speed, the minimum wind speed at which the wind turbine begins generating electricity, is fixed at 3 m / s based on the selected wind turbine model. When the wind speed is below this value, the wind turbine cannot start, and its output is zero. The wind turbine cut-out speed, the maximum wind speed at which the wind turbine stops generating electricity, is fixed at 25 m / s. When the wind speed is above this value, the wind turbine stops to ensure equipment safety, and its output is also zero. The wind turbine rated speed, the wind speed at which the wind turbine reaches its rated output, is fixed at 13 m / s. When the wind speed reaches this value, the wind turbine's output stabilizes at the rated output level. The wind turbine rated output, fixed at 15 MW based on the installed capacity, is the maximum active power that the wind turbine can output. In implementation, real-time wind speed data is first collected in step S2. Then, the corresponding calculation method is selected according to the wind speed range: when the wind speed is below 3 m / s or above 25 m / s, the output is directly determined to be 0; when the wind speed is between 3 and 13 m / s, the output is calculated according to a specific functional relationship; when the wind speed is between 13 and 25 m / s, the output is taken as the rated output of 15 MW. This model can accurately describe the wind power output under different wind speeds, providing accurate wind power output data support for step S1 to construct the electric and thermal coupling system framework, step S4 to establish the optimization model, and step S5 to solve the problem.

[0030] Preferably, the photovoltaic output model in S1 is as follows: ,in, Indicates the first Solar power output during specific time periods Indicates the conversion efficiency of photovoltaic modules. Indicates the first Solar irradiance during the time period Indicates the area of ​​the photovoltaic array. Indicates the temperature coefficient of photovoltaic modules. Indicates the first ambient temperature during the period This indicates the efficiency of photovoltaic modules under standard test conditions. Indicates solar irradiance under standard test conditions. This indicates the ambient temperature under standard test conditions.

[0031] Specifically, in the photovoltaic (PV) output model, the parameters for PV output in time period t are affected by factors such as solar irradiance and ambient temperature, with a maximum value not exceeding the rated output of the PV array. The PV module conversion efficiency is fixed at 22% based on the characteristics of the selected monocrystalline silicon module. This parameter reflects the PV module's ability to convert solar energy into electrical energy and is one of the core parameters affecting PV output. The solar irradiance in time period t is collected by an irradiance meter deployed in the PV power plant in step S2, with a value ranging from 0 to 1000 watts per square meter, a collection frequency of once every 5 minutes, and a data accuracy of 1 watt per square meter. Higher irradiance results in greater PV output. The PV array area is fixed at 50,000 square meters based on the installed capacity of the PV power plant and is a fundamental parameter for calculating the total solar energy that the PV array can receive. The PV module temperature coefficient is fixed at -0.38% / ℃. This negative value indicates that the conversion efficiency decreases as the module temperature increases, making it a key parameter reflecting the negative impact of temperature on PV output. The ambient temperature at time t is collected by a temperature sensor deployed in step S2, with a range of -20℃ to 40℃, collected every 5 minutes, and a data accuracy of 0.1℃. Ambient temperature indirectly affects photovoltaic output by influencing module temperature. Under standard test conditions, the photovoltaic module efficiency is fixed at 25%, solar irradiance is fixed at 1000 W / m², and ambient temperature is fixed at 25℃. These parameters provide a benchmark for calculating the impact of temperature on module efficiency. In implementation, real-time irradiance and ambient temperature data are collected first, then the actual module efficiency is calculated using the fixed parameters, and finally the photovoltaic output at time t is calculated using a formula. This output data is used for system framework construction in step S1, model constraint setting in step S4, and solution process in step S5, ensuring that the model conforms to the actual operating characteristics of photovoltaics.

[0032] Preferably, the heat storage change model of the thermal storage device in S1 is as follows: ,in, Indicates the first The time-slot thermal storage device stores heat in real time. Indicates the first The time-slot thermal storage device stores heat in real time. Indicates the first The charging power of the time-limited thermal storage device Indicates time interval, This indicates the heat charging efficiency of the thermal storage device. Indicates the first Heat release power of time-limited thermal storage device This indicates the heat release efficiency of the thermal storage device.

[0033] Specifically, the model for the change in heat storage capacity of the thermal storage device includes a parameter for the real-time heat storage capacity of the device in time period t, with values ​​ranging from 10 MWh to 50 MWh, corresponding to the operating temperature range of 80°C to 120°C. This parameter directly reflects the current energy storage status of the thermal storage device. The real-time heat storage capacity of the device in time period t-1 is the heat storage data at the end of the previous time period, also ranging from 10 MWh to 50 MWh, serving as the basis for calculating the heat storage capacity in the current time period. This data is acquired through the liquid level sensor or temperature sensor of the thermal storage device, with a data accuracy of 0.1 MWh. The thermal storage device's charging power in time period t ranges from 0 to 8 MW. This parameter is positive when the device is charging, and the specific value is determined based on the system's heat load demand and the electric boiler's heating capacity, controlled by adjusting the charging circulation pump speed. The thermal storage device's releasing power in time period t also ranges from 0 to 8 MW. This parameter is positive when the device is releasing heat, and the specific value is determined based on the heat load demand, controlled by adjusting the releasing circulation pump speed. The time interval is fixed at 15 minutes (0.25 hours), consistent with the time intervals of the trading and forecasting periods, ensuring time consistency. The thermal storage device's charging efficiency is fixed at 90%, reflecting the degree of energy loss during charging; the thermal storage device's releasing efficiency is fixed at 85%, reflecting the degree of energy loss during releasing heat. Both are determined by the thermal storage device's insulation performance and heat exchange structure. During implementation, the heat storage data of the previous period is first obtained, and then the heat storage power is determined according to the heat storage status of the current period. Combining the time interval and heat storage efficiency, the change in heat storage in the current period is calculated. Finally, the heat storage of the previous period is added to the change (addition for heat storage and subtraction for heat release) to obtain the real-time heat storage of the current period. This data is used for setting equipment constraints in step S1, collecting equipment status data in step S2, and formulating scheduling strategies in step S6 to ensure that the thermal storage device operates within a safe range.

[0034] Preferred, such as Figure 2 As shown, S2 includes the following sub-steps: S21, obtaining spot electricity price data for each trading period within the next day through the electricity market trading platform interface, and simultaneously collecting demand signals released by the frequency regulation and backup ancillary service markets, including the required service capacity and corresponding revenue standards, to establish an electricity market data ledger; S22, using sensors deployed in wind farms and photovoltaic power stations to collect wind power and photovoltaic output data in real time, with a sampling interval set to 15 minutes, and simultaneously collecting heat load demand and electricity load demand data through the user-side energy management system, recording the peak and valley values ​​of demand for each load period; S23, obtaining the current heat storage capacity of the thermal storage device through the equipment status monitoring module, reading the remaining power and state of charge of the electrochemical energy storage through the energy storage management system, and collecting its operating parameters through the electric boiler control system, including the current operating power, number of start-stop cycles, and cumulative operating time, to ensure the completeness and real-time nature of the data collection for each device status.

[0035] Specifically, in the multi-dimensional data acquisition process of step S2, in step S21, data is obtained through the API interface of the power market trading platform. The interface access frequency is set to once every 15 minutes, synchronized with the trading period. The acquired spot electricity price data must include the specific values ​​for 96 time periods in the next day, and the data accuracy is retained to two decimal places. The collection of ancillary service demand signals needs to distinguish between frequency regulation and standby services. For frequency regulation services, the minimum required capacity and corresponding capacity and mileage price for each time period must be recorded. For standby services, the minimum required capacity and corresponding price for each time period must be recorded. After collection, a structured ledger is established and stored in the format of "date-time period-data type-value". In step S22, the sampling interval for wind power and photovoltaic data is set to 5 minutes, the accuracy of wind power output data is 0.1 MW, the accuracy of photovoltaic output data is 0.1 MW, the accuracy of solar irradiance data is 1 W / m², and the accuracy of ambient temperature data is 0.1℃. The sampling interval for load data is 15 minutes, the accuracy of electrical load is 0.01 MW, and the accuracy of thermal load is 0.01 MWh. At the same time, the specific time periods (accurate to the minute) and values ​​of load peak and valley values ​​are recorded. In step S23, the data acquisition interval for the thermal storage device is 10 minutes, with a thermal storage capacity accuracy of 0.1 MWh and a temperature accuracy of 0.1℃; the data acquisition interval for electrochemical energy storage is 5 minutes, with a remaining power accuracy of 0.1 MWh and a state of charge accuracy of 1%; the data acquisition interval for the electric boiler is 10 minutes, with an operating power accuracy of 0.1 MW, a cumulative start-stop count accuracy of 1 time, and a cumulative operating time accuracy of 1 minute. After all data acquisition, outlier verification must be performed to remove data that exceeds reasonable ranges (such as wind power output exceeding the rated value or temperature exceeding the equipment's operating range) to ensure reliability.

[0036] Preferred, such as Figure 3 As shown, S3 includes the following sub-steps: S31, based on the historical wind power output data and historical wind speed data collected in S2, a wind power output prediction model is established using time series analysis, and the wind speed prediction data for the next day is input to obtain the predicted wind power output values ​​for each time period; S32, combining the historical photovoltaic output data, solar irradiance, and ambient temperature data collected in S2, a photovoltaic output prediction model is constructed using machine learning algorithms, and the solar irradiance and ambient temperature prediction data for the next day released by the meteorological department are imported to generate the predicted photovoltaic output values ​​for each time period; S33, based on the historical heat load and electricity load demand data obtained in S2, considering seasonal, holiday, and user energy consumption habits, a regression analysis method is used to establish heat load and electricity load demand prediction models respectively, and the predicted heat load and electricity load demand values ​​for each time period in the next day are output; S34, referring to the historical electricity price data and historical demand and price data of ancillary services in the electricity market, combined with macroeconomic factors, energy policies, and grid operation status, a trend prediction method is used to predict the spot electricity price, frequency regulation and reserve service demand, and corresponding prices for each trading period in the next day.

[0037] Specifically, in the multi-dimensional data prediction process of step S3, in step S31, the ARIMA method is used for wind power prediction. The data sample is historical data (no less than 17,280 sets) taken once every 5 minutes for the past 3 months. The maximum number of iterations during model training is set to 1,000, and the convergence accuracy is 10^-6. The input future wind speed data must come from authoritative meteorological departments, with a time resolution of 15 minutes and an accuracy of 0.1 m / s. The prediction output is the wind power output value for 96 time periods, with the error controlled within ±8%. If the prediction error exceeds the range for a certain time period, the model parameters (such as the autoregression order and the moving average order) need to be readjusted before prediction is made again. In step S32, photovoltaic (PV) forecasting uses a random forest algorithm with historical data (at least 17,280 sets) collected every 5 minutes over the past 3 months. The algorithm uses 100 decision trees with a maximum depth of 15 layers. The input future irradiance and temperature data have a time resolution of 15 minutes, with accuracies of 1 watt / m² and 0.1°C, respectively. The predicted output includes PV power output values ​​for 96 time periods, with errors controlled within ±10%. If the error exceeds this limit, the sample size needs to be increased (e.g., by adding data from the past month) and the model retrained. In step S33, load forecasting uses multiple linear regression with historical data (at least 34,560 sets) collected every 15 minutes over the past 6 months. The seasonal coefficients for spring, summer, autumn, and winter are set to 0.9, 1.2, 0.8, and 1.5, respectively. Date types are coded as weekdays (1) and holidays (0). The input future date type and temperature data output heat and electricity load values ​​for 96 time periods, with errors controlled within ±7% and ±5%, respectively. In step S34, market data forecasting combines grey forecasting and trend extrapolation. The sample consists of historical data updated every 15 minutes for the past 6 months (no less than 34,560 sets). It also incorporates influencing factors such as coal price index (monthly update), GDP growth rate (quarterly update), and grid load rate (daily update). The forecast outputs spot electricity prices (error ±6%), frequency regulation and reserve demand and prices (error ±5%, ±8%) for 96 time periods. All forecast results need to be compared and verified with historical data for the same period to ensure reasonableness.

[0038] Preferred, such as Figure 4As shown, S4 includes the following sub-steps: S41, clarifying the composition of the system's comprehensive revenue, incorporating spot market revenue, frequency regulation market revenue, reserve market revenue, and heating revenue into the objective function, determining the weight of each revenue item in the total revenue, and ensuring that the objective function can comprehensively reflect the system's revenue situation; S42, for spot market revenue, constructing a sub-model for calculating spot market revenue based on the predicted spot electricity price for each time period of the next day and the range of tradable electricity in the system, clarifying the correlation between tradable electricity and electricity price; S43, for frequency regulation market revenue, establishing a sub-model for calculating frequency regulation market revenue by combining the predicted value of the system's bid-winning frequency regulation capacity, the predicted value of the winning bid price in the frequency regulation capacity market, and the predicted value of the frequency regulation mileage and the predicted value of the frequency regulation mileage market price for each time period; S44, constructing a sub-model for calculating reserve market revenue based on the predicted value of reserve capacity demand and the predicted value of reserve capacity price for each time period in the reserve market; S45, establishing a sub-model for calculating heating revenue based on the predicted value of heat load demand and the predicted value of heating price for each time period of the next day, and finally integrating the sub-models to form a complete objective function for the collaborative trading strategy optimization model.

[0039] Specifically, in the collaborative transaction optimization model construction process of step S4, in step S41, the system's comprehensive revenue includes four categories: spot market revenue, frequency regulation revenue, reserve revenue, and heating revenue. The weights are set according to market activity and revenue potential as follows: spot market 0.4, frequency regulation 0.25, reserve revenue 0.2, and heating revenue 0.15. The weights need to be determined with reference to the average revenue share of each market over the past three months. If the revenue share of a certain market fluctuates by more than 10%, the weights need to be readjusted. The objective function expression needs to clearly define the mathematical relationship between each revenue item and the decision variables (traded electricity volume, capacity, and heating revenue) to ensure that it can be quantified and calculated. In step S42, the spot market revenue sub-model is based on "time-of-use electricity price × traded electricity volume". The traded electricity volume range is set from -10 MWh (electricity purchase) to 20 MWh (electricity sale). It needs to be linked to the predicted value of renewable energy output and the predicted value of electricity load to ensure that the traded electricity volume does not exceed the system's regulation capacity (e.g., the electricity sale volume can be increased when renewable energy output is high). In step S43, the frequency regulation revenue sub-model is divided into two parts: capacity (bid-increased capacity × capacity price) and mileage (time-period mileage × mileage price). The bid-increased capacity ranges from 0 to 10 MW, and needs to be determined in conjunction with the adjustable capacity of electrochemical energy storage and electric boilers (energy storage charging and discharging power upper limit of 10 MW, electric boiler power adjustment range of 3-10 MW). The mileage calculation needs to be associated with the allowable range of grid frequency deviation (±0.2 Hz) and system response time (≤1 second). In step S44, the standby revenue sub-model is calculated using "time-period standby capacity × standby price". The standby capacity ranges from 0 to 15 MW, and needs to meet equipment start-up and shutdown constraints (such as electric boiler start-up and shutdown interval ≥30 minutes) to ensure that the equipment can respond in a timely manner when called. In step S45, the heating revenue sub-model is calculated using "time-period heating capacity × heating price". When the heating capacity ranges from 0 to 8 MW, it needs to match the predicted heat load value and associate it with the charging and discharging capacity of the thermal storage device (charging and discharging power upper limit of 8 MW). Finally, the sub-models are integrated to form a complete optimization model containing the objective function and all constraints.

[0040] Preferred, such as Figure 5As shown, step S5 includes the following sub-steps: S51, defining variables for the collaborative trading strategy optimization model constructed in S4, clarifying that the decision variables are the system's participation in the spot, frequency regulation, and reserve markets, and determining the value range of each decision variable; S52, converting the equipment operation constraints established in S1 into mathematical constraints, including upper and lower limits for wind power output, upper and lower limits for photovoltaic power output, power and start-up / shutdown constraints for electric boilers, capacity, charging and discharging power and temperature constraints for thermal storage devices, and state of charge and charging / discharging power constraints for electrochemical energy storage, ensuring the accuracy of the constraints. Accuracy and executability; S53, select the intelligent optimization algorithm, set the algorithm's iteration number, population size, and convergence accuracy parameters, and input the prediction data obtained in S3 and the constraints established in S52 into the model; S54, run the intelligent optimization algorithm to solve the model, monitor the algorithm's convergence in real time during the solution process, and adjust the algorithm parameters if slow convergence or getting stuck in a local optimum is observed until the optimal solution that satisfies the constraints and maximizes the system's overall benefit is obtained, and determine the system's capacity and heat supply for each time period in the next day in the spot, frequency regulation, and reserve markets.

[0041] Specifically, step S5 involves solving the model through four sub-steps, each specifying the algorithm parameters and solution control. In S51, the decision variables are defined as the spot trading electricity volume, frequency regulation capacity, reserve capacity, and heat supply (a total of 384 variables) for the next 96 time periods, with value ranges of -10 to 20 MWh, 0 to 10 MWh, 0 to 15 MWh, and 0 to 8 MWh, respectively. These ranges are determined with reference to the rated parameters of the equipment (energy storage capacity of 20 MWh and heat storage capacity of 50 MWh) and market rules (reserve capacity ≤ 5% of regional load). In S52, the equipment constraints of S1 are transformed into mathematical conditions: wind power output is within ±8% of the predicted value in S3 and the ramp rate is ≤5% / minute; photovoltaic output is within ±10% of the predicted value; electric boiler power is 3-10 MW and the start-stop interval is ≥30 minutes; thermal storage device has a heat storage capacity of 10-50 MWh, a charge / discharge power of ≤8 MW, and a temperature of 80-120℃; electrochemical energy storage has a state of charge of 20%-90% and a charge / discharge power of ≤10 MW, ensuring that the constraints are consistent with actual operation. In S53, an improved particle swarm optimization algorithm is selected, with parameters set as follows: population size 200 (0.5 times the number of variables), number of iterations 500, convergence accuracy 10^-6, inertia weight initially 0.9 and linearly reduced to 0.4, crossover probability 0.7, mutation probability 0.05, and learning factors 1 and 2 both 2. The predicted data of S3 and the constraints of S52 are transformed into an input format that the algorithm can recognize. In S54, the population is first initialized to calculate the fitness value (overall benefit), the optimal particle is updated and its position is adjusted, and if it exceeds the range, it is truncated; during the iteration, convergence is monitored. If the change of the optimal value is less than 10^-6 for 20 consecutive iterations, convergence is achieved. If convergence is not achieved after 300 iterations, the crossover probability is increased to 0.8 and the mutation probability is increased to 0.08; after solving, the optimal decision variables are output to verify whether all constraints are satisfied. If not, the parameters are adjusted and the solution is resolved until a feasible optimal solution is obtained.

[0042] This paper proposes a method for optimizing power operation and trading strategies through the coordinated participation of new energy power and heat coupling. It constructs a framework for the coordinated operation of a power and heat coupling system integrating wind power, photovoltaics, electric boilers, thermal storage devices, and electrochemical energy storage. By clearly defining the operational constraints of various devices, it achieves the organic integration and coordinated linkage of multi-device resources, avoiding resource waste from independent equipment operation. By collecting multi-dimensional operational data such as power market, energy supply and demand, and equipment status, and predicting relevant data for future periods, it provides accurate data support for subsequent strategy optimization, improving the scientific nature of decision-making. Finally, it constructs an optimization model that maximizes the overall system benefit, including multi-market and heating revenue, and solves it with intelligent optimization algorithms. This model clarifies the system's capacity and heating capacity participating in multiple markets at different times, helping the system achieve higher overall benefits while ensuring stable operation.

[0043] This method comprehensively integrates multiple types of energy equipment to build a complete electric-thermal coupling and coordinated operation framework, breaking down the barriers to independent operation of electric and thermal systems, improving the coordination between equipment, and enabling it to adapt to complex operational needs in multi-market trading environments. Addressing the problem of traditional optimization having only one dimension, this method comprehensively considers the revenue composition of multiple markets, including spot markets, frequency regulation, and reserves, while fully incorporating the operational constraints of various equipment such as wind power, photovoltaics, and electric boilers. This ensures that the optimization results can both take into account the overall system revenue and guarantee operational stability, providing more comprehensive strategic support for new energy electric-thermal coupling systems.

[0044] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0045] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing the strategy of new energy electric heat coupling collaborative participation in power operation and transaction, characterized in that, The method comprises the following steps: S1, constructing an electric and thermal coupling system cooperative operation framework, integrating wind power, photovoltaic, electric boiler, heat storage device and electrochemical energy storage, and establishing models of each device element, wherein the wind power output model is divided into four types of output states according to wind speed, i.e., below the cut-in wind speed, between the cut-in and rated wind speeds, between the rated and cut-out wind speeds, and above the cut-out wind speed, the photovoltaic output model is constructed in combination with solar irradiance, ambient temperature and component efficiency, and meanwhile, operation constraints of each device are determined, the wind power constraints include upper and lower limits of output and climbing limit, the photovoltaic constraints include upper and lower limits of output, the electric boiler constraints include power range and start-stop interval limit, the heat storage device constraints include capacity, charging and discharging power and temperature limit, and the electrochemical energy storage constraints include state of charge range and charging and discharging power limit; S2, collecting multi-dimensional operation data, including spot electricity price, auxiliary service demand and income standard of the electricity market, real-time output of wind power and photovoltaic, heat load and electricity load demand of energy supply and demand, and residual heat storage amount, residual energy storage amount and electric boiler working condition of device state; S3, predicting related data in a future period based on the collected data, including spot electricity price, frequency modulation and standby service demand and price, wind power and photovoltaic output, heat load and electricity load demand in each trading period in a future day; S4, constructing a cooperative transaction strategy optimization model with the maximum system comprehensive income as the target, and the target function includes spot market, frequency modulation market, standby market and heat supply income; S5, solving the model by using an intelligent optimization algorithm, determining the capacity of the system participating in the spot market, frequency modulation market and standby market and the heat supply amount in each period in a future day in combination with the device constraints in S1 and the predicted data in S3; S6, generating an electric and heat cooperative dispatching strategy according to the solving result, controlling the operation state of each device, and performing system operation and transaction in the electricity market.

2. The method for optimizing the strategy of new energy electric heating coupling cooperative participation in power operation and transaction according to claim 1, characterized in that, The total income calculation model of the spot market in the S4 is wherein, represents the total income of the spot market, represents the serial number of the trading period within the next day, represents the total number of trading periods within the next day, represents the spot electricity price of the th trading period, represents the trading electricity quantity of the th trading period in which the system participates in the spot market.

3. The method of claim 1, wherein the method is characterized by, The total revenue calculation model of the frequency modulation market in S4 is Wherein, represents the total revenue of the frequency modulation market, represents the frequency modulation capacity bid in the system in the future day, represents the bid price in the frequency modulation capacity market, represents the sequence number of the frequency modulation response period in the future day, represents the total number of frequency modulation response periods in the future day, represents the frequency modulation mileage of the first period, represents the frequency modulation mileage of the first period frequency modulation mileage market price.

4. The new energy electric heating coupling co-participation power operation and transaction strategy optimization method according to claim 1, characterized in that, The S1 wind power output model is wherein, denotes the wind power output of the time period, denotes the wind speed of the time period, denotes the wind turbine cut-in wind speed, denotes the wind turbine cut-out wind speed, denotes the wind turbine rated wind speed, denotes the wind turbine rated power.

5. The new energy electric heating coupling co-participation power operation and transaction strategy optimization method according to claim 1, characterized in that, The photovoltaic power output model in S1 is wherein, denotes the photovoltaic power output in the time period, denotes the photovoltaic module conversion efficiency, denotes the solar irradiance in the time period, denotes the photovoltaic array area, denotes the photovoltaic module temperature coefficient, denotes the ambient temperature in the time period, denotes the photovoltaic module efficiency under standard test conditions, denotes the solar irradiance under standard test conditions, denotes the ambient temperature under standard test conditions.

6. The new energy electric heating coupling co-participation power operation and transaction strategy optimization method according to claim 1, characterized in that, The heat storage amount change model of the heat storage device in the S1 is wherein, represents the real-time heat storage amount of the heat storage device in the first time interval, represents the real-time heat storage amount of the heat storage device in the second time interval, represents the heat charging power of the heat storage device in the first time interval, represents the time interval, represents the heat charging efficiency of the heat storage device, represents the heat releasing power of the heat storage device in the first time interval, represents the heat releasing efficiency of the heat storage device.

7. The new energy electric heating coupling co-participation power operation and transaction strategy optimization method according to claim 1, characterized in that, The S2 comprises the following steps: S21, acquiring spot electricity price data in each trading period in a future day through an interface of an electricity market transaction platform, collecting demand signals published by frequency modulation and standby auxiliary service markets, including required service capacity and corresponding income standard, and establishing an electricity market data account book; S22, collecting output data of wind power and photovoltaic by using sensors deployed in wind farms and photovoltaic power stations in real time, setting a sampling interval of 15 minutes, collecting heat load demand and electricity load demand data through a user-side energy management system, and recording demand peak and valley values in each load period; S23, acquiring the current residual heat storage amount of the heat storage device through a device state monitoring module, reading the residual energy amount and state of charge of the electrochemical energy storage through an energy storage management system, and collecting operating condition parameters of the electric boiler, including current operating power, start-stop times and cumulative operating time, to ensure the completeness and real-time performance of the data collection of each device state.

8. The new energy electric heating coupling co-participation power operation and transaction strategy optimization method according to claim 1, characterized in that, The S3 comprises the following steps: S31, establishing a wind power output prediction model by using a time series analysis method based on the wind power historical output data and wind speed historical data collected in S2, inputting wind speed prediction data in a future day, and obtaining predicted values of wind power output in each period; S32, combined with the photovoltaic historical output data, solar radiation and environmental temperature history data collected in S2, a photovoltaic output prediction model is constructed by using machine learning algorithm, the future one-day solar radiation and environmental temperature prediction data published by the meteorological department are imported, and the photovoltaic output prediction value of each period is generated; S33, according to the thermal load and electrical load historical demand data obtained in S2, considering the factors of season, holiday and user energy consumption habit, the regression analysis method is used to establish the thermal load and electrical load demand prediction model respectively, and the thermal load and electrical load demand prediction value of each period in the future one day is output; S34, referring to the historical electricity price data and auxiliary service historical demand and price data of the electricity market, combined with macroeconomic factors, energy policy and power grid operation condition, the trend prediction method is used to predict the spot electricity price, frequency modulation and standby service demand and corresponding price of each trading period in the future one day. 9.The new energy electric heating coupling co-participation power operation and transaction strategy optimization method according to claim 1, characterized in that, The S4 includes the following steps: S41, the composition of system comprehensive income is determined, the spot market income, frequency modulation market income, standby market income and heat supply income are included in the objective function, the weight of each income item in the total income is determined, and the objective function is ensured to fully reflect the system income situation; S42, for the spot market income, according to the spot electricity price prediction value of each period in the future one day and the system tradable electricity range, the spot market income calculation sub model is constructed, and the correlation between the transaction electricity and the price is determined; S43, for the frequency modulation market income, combined with the system winning frequency modulation capacity prediction value, the frequency modulation capacity market winning price prediction value, the frequency modulation mileage prediction value of each period and the frequency modulation mileage market price prediction value, the frequency modulation market income calculation sub model is established; S44, according to the standby capacity demand prediction value and standby capacity price prediction value of each period in the standby market, the standby market income calculation sub model is constructed; S45, according to the heat load demand prediction value of each period in the future one day and the heat supply price prediction value, the heat supply income calculation sub model is established, and finally the complete collaborative trading strategy optimization model objective function is formed by integrating each sub model.

10. The method of claim 1, wherein the new energy electric heating coupling co-participation power operation and transaction strategy optimization method is characterized in that, The S5 includes the following steps: S51, the collaborative trading strategy optimization model constructed in S4 is subjected to variable definition, the decision variables are determined as the capacity of the system participating in the spot, frequency modulation and standby market and the heat supply amount, and the value range of each decision variable is determined; S52, the constraints of each device operation established in S1 are converted into mathematical constraint conditions, including the upper and lower limits of wind power output, the upper and lower limits of photovoltaic output, the power and start-stop constraints of electric boiler, the capacity, charging and heating power and temperature constraints of heat storage device, and the state of charge and charging and discharging power constraints of electrochemical energy storage, so as to ensure the accuracy and executability of the constraint conditions; S53, an intelligent optimization algorithm is selected, the iteration number, population size and convergence precision parameters of the algorithm are set, the prediction data obtained in S3 and the constraint conditions established in S52 are input into the model; S54, the intelligent optimization algorithm is run to solve the model, and convergence of the algorithm is monitored in real time during the solving process. If slow convergence or falling into local optimum occurs, the algorithm parameters are adjusted until an optimal solution is obtained that satisfies the constraint conditions and maximizes the overall revenue of the system, and the capacity of the system participating in the spot market, frequency modulation market and standby market and the heat supply in each period within the next day are determined.

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