Multi-time scale new energy-storage combined output optimization method and system based on spot electricity price

Abstract

The application relates to the technical field of power system optimization scheduling, and provides a new energy-energy storage combined output optimization method and system based on multi-time scale spot electricity prices, which are used to solve the problems of poor system operation stability and low new energy regulation accuracy in the prior art. The method comprises the following steps: detecting the temperature change rate of the composite phase change material charging and discharging process to determine the heat storage capacity state of the energy storage unit, combining day-ahead electricity price data to generate a day-ahead joint scheduling plan in a coordination optimization framework, which takes into account the operation cost and the new energy consumption rate; constructing an elasticity matrix based on historical power consumption data, correcting the elasticity matrix by using the latest market information and system state, and determining the load adjustment amount by combining the intraday electricity price; based on the real-time electricity price and the load adjustment amount, the real-time output adjustment instruction of the new energy unit and the energy storage is calculated by using a model predictive control algorithm, the operation state of the new energy unit and the energy storage is synchronously adjusted, and the collaborative optimization is realized. The application improves the system operation stability and the new energy regulation accuracy.

Description

Technical Field

[0001] This application relates to the field of power system optimization and dispatch technology, and in particular to a new energy-energy storage joint output optimization method and system based on spot electricity prices across multiple time scales. Background Technology

[0002] With the continuous increase in the proportion of renewable energy generation and the advancement of electricity spot market construction, regional energy systems face the dual challenges of uncertainty in renewable energy output and volatility in market electricity prices during operation. Against this backdrop, there is a need for an optimized dispatching method that can adapt to electricity price signals across multiple time scales, effectively coordinate the collaborative operation of renewable energy and energy storage devices, and rapidly respond to changes in system status, in order to maintain system power balance and improve the efficiency of clean energy utilization.

[0003] One existing solution is a predictive control method based on a fixed model. This method establishes a statistical prediction model for renewable energy output, combines it with a simplified linear model of the energy storage system, uses market electricity prices as an economic signal, formulates a preliminary dispatch plan during the day-ahead phase, and performs linear feedback correction based on prediction deviations during real-time operation. This solution relies on a pre-set optimization algorithm to periodically adjust the system's operating state.

[0004] However, such solutions exhibit certain limitations in complex operating environments. The actual energy storage and release capacity of energy storage devices dynamically changes with operating conditions and material states, and fixed models struggle to accurately reflect the time-varying nature of this physical characteristic. Furthermore, the spatiotemporal differences in the response of user-side loads to electricity prices are insufficiently reflected in the scheduling model, leading to response deviations between real-time adjustment commands and the actual system operating state. The transitions between states during multi-timescale optimization processes are not smooth enough, affecting the stability and control accuracy of the system. Summary of the Invention

[0005] This application provides a method and system for optimizing the combined output of new energy and energy storage based on spot electricity prices across multiple time scales, in order to solve the problems of poor system operation stability and low precision of new energy regulation in the prior art.

[0006] To address the aforementioned technical problems, in a first aspect, this application provides a multi-timescale method for optimizing the combined output of new energy and energy storage based on spot electricity prices, comprising:

[0007] Acquire composite phase change materials for energy storage systems in regional energy systems, time-of-use electricity price data from the electricity spot market, and historical electricity consumption data. The time-of-use electricity price data includes day-ahead electricity price data, intraday electricity price data, and real-time electricity price data.

[0008] By detecting the temperature change rate of the composite phase change material during the charging and discharging processes, the thermal storage capacity status of the energy storage unit is determined. Based on the day-ahead electricity price data and the thermal storage capacity status, a day-ahead joint scheduling plan for new energy units and energy storage systems is generated within a multi-timescale coordination and optimization framework, with the objectives of optimizing the total operating cost of the regional energy system and maximizing the new energy absorption rate.

[0009] Based on the historical electricity consumption data, the elasticity coefficients of electricity prices for different time periods are calculated, and an elasticity matrix is ​​formed based on the elasticity coefficients.

[0010] By combining the latest electricity spot market information and regional energy system operation status data, the parameters of the elasticity matrix are corrected, and the load adjustment amount is determined based on the intraday electricity price data and the corrected elasticity matrix.

[0011] Based on the real-time electricity price data and the load adjustment amount, the model predictive control algorithm is used to calculate the real-time output adjustment instructions of the new energy generating units and the energy storage system in a rolling manner. According to the real-time output adjustment instructions, the output level of the new energy generating units and the charging and discharging power of the energy storage system are adjusted synchronously to achieve the joint output optimization of new energy and energy storage.

[0012] Optionally, the step of determining the thermal storage capacity status of the energy storage unit by detecting the temperature change rate of the composite phase change material during the charging and discharging processes, and generating a day-ahead joint dispatch plan for the new energy unit and the energy storage system based on the day-ahead electricity price data and the thermal storage capacity status, within a multi-timescale coordinated optimization framework, with the objectives of optimizing the total operating cost of the regional energy system and maximizing the renewable energy absorption rate, includes:

[0013] The surface temperature of the composite phase change material is measured during the charging and discharging processes, and the surface temperature measurement values ​​are recorded within a predetermined time window.

[0014] Based on the surface temperature measurement, the first temperature change rate of the composite phase change material during the charging process and the second temperature change rate during the releasing process are calculated.

[0015] Establish a multi-timescale coordination and optimization framework that includes the day-ahead phase;

[0016] In the day-ahead phase of the multi-timescale coordinated optimization framework, the thermal storage capacity status is determined based on the first temperature change rate and the second temperature change rate. Combined with the day-ahead electricity price data, an optimization objective function is constructed that includes the total operating cost of the regional energy system and the renewable energy absorption rate. Based on the optimization objective function, a day-ahead joint dispatch plan is generated.

[0017] Optionally, the step of determining the thermal storage capacity status based on the first temperature change rate and the second temperature change rate, and constructing an optimization objective function that includes the total operating cost of the regional energy system and the renewable energy absorption rate in conjunction with the day-ahead electricity price data, and generating a day-ahead joint dispatch plan based on the optimization objective function, includes:

[0018] The first temperature change rate is compared with a preset first reference rate range, and the second temperature change rate is compared with a preset second reference rate range. Based on the comparison result, the thermal storage capacity status of the energy storage unit is determined, wherein the upper and lower limits of the second reference rate range are numerically smaller than the upper and lower limits of the first reference rate range.

[0019] Based on the thermal storage capacity status, determine the maximum charging power limit and the maximum discharging power limit that the energy storage unit can provide during the scheduling cycle;

[0020] Using the maximum charging power limit and the maximum discharging power limit as operating constraints for the energy storage unit, and combining the day-ahead electricity price data, an optimization objective function is constructed that includes the total operating cost of the regional energy system and the renewable energy absorption rate.

[0021] Solving the optimization objective function yields the planned output value of the new energy unit in each time period within the scheduling cycle, as well as the planned charging power value and planned discharging power value of the energy storage system in each time period within the scheduling cycle.

[0022] Based on the planned output value, the planned charging power value, and the planned discharging power value, a day-ahead joint scheduling plan is formed.

[0023] Optionally, solving the objective function to obtain the planned output value of the new energy generating unit in each time period within the scheduling cycle, and the planned charging power value and planned discharging power value of the energy storage system in each time period within the scheduling cycle, includes:

[0024] The operating cost item and the new energy consumption item in the optimization objective function are combined into a comprehensive optimization index;

[0025] Based on the day-ahead electricity price data, the electricity price weighting coefficient for each time period is determined, and the comprehensive optimization index is weighted based on the electricity price weighting coefficient.

[0026] Using the predicted output range of the new energy generating units as the unit output constraint, the optimal solution of the weighted comprehensive optimization index is obtained through iterative calculation under the operating constraint and the unit output constraint.

[0027] Based on the optimal solution, the planned output values ​​of the new energy generating units in each time period of the scheduling cycle are obtained, as well as the planned charging power value and planned discharging power value of the energy storage system in each time period of the scheduling cycle.

[0028] Optionally, the step of calculating the elasticity coefficients of electricity prices for multiple time periods based on the historical electricity consumption data, and forming an elasticity matrix based on the elasticity coefficients, includes:

[0029] Extract historical electricity price data and corresponding historical electricity consumption data for each predetermined time period from multiple historical days from the historical electricity consumption data;

[0030] For two adjacent time periods, based on the historical electricity price data and the corresponding historical electricity consumption data, the electricity price difference between the first time period and the second time period is calculated, and the electricity price difference is used as the change in electricity price during the time period. The user electricity consumption difference between the first time period and the second time period is also calculated, and the user electricity consumption difference is used as the change in electricity consumption during the time period.

[0031] Based on the change in electricity price and the change in electricity consumption during the specified time period, calculate the elasticity coefficient of electricity consumption to electricity price for each type of user during different time periods.

[0032] Multiple elastic coefficients belonging to the same type of user are arranged in order of time period and combined to form the elastic vector of the corresponding user;

[0033] By combining the elasticity vectors of all user categories, an elasticity matrix is ​​constructed that reflects the relationship between user electricity consumption behavior and electricity price changes in the entire regional energy system.

[0034] Optionally, the step of combining the latest electricity spot market information and regional energy system operation status data to adjust the parameters of the elasticity matrix, and determining the load adjustment amount based on the intraday electricity price data and the adjusted elasticity matrix, includes:

[0035] Based on the supply and demand tension index in the latest electricity spot market information, a first adjustment factor is generated, and based on the first adjustment factor, the elasticity vector of the corresponding time period in the elasticity matrix is ​​corrected for the first time.

[0036] Based on the deviation between the real-time output and the predicted output of the new energy source in the operating status data, a second adjustment factor is generated. Based on the second adjustment factor, the elastic vector that has been corrected in the first correction is corrected in the second correction to obtain the corrected elastic matrix.

[0037] Based on the intraday electricity price data and the corrected elasticity matrix, calculate the amount of user load change required to achieve system power balance during the current intraday period;

[0038] The user load change is allocated according to user category to determine the load adjustment amount for each user category.

[0039] Optionally, the step of using a model predictive control algorithm to calculate the real-time output adjustment command of the new energy generating units and energy storage system based on the real-time electricity price data and the load adjustment amount includes:

[0040] Based on the real-time electricity price data and the load adjustment amount, a real-time optimization problem including system power balance constraints is constructed;

[0041] By using model predictive control algorithms, the real-time optimization problem is solved in a rolling manner to obtain the planned output adjustment sequence of the new energy generating units and the planned power adjustment sequence of the energy storage system for several future periods.

[0042] The adjustment amount of the first time period is extracted from the planned output adjustment sequence as the real-time output adjustment instruction for the new energy unit, and the adjustment amount of the first time period is extracted from the planned power adjustment sequence as the real-time power adjustment instruction for the energy storage system.

[0043] The real-time output adjustment command of the new energy unit and the real-time power adjustment command of the energy storage system are combined to form a real-time output adjustment command.

[0044] Secondly, this application provides a new energy-energy storage joint output optimization system based on spot electricity prices across multiple time scales, including:

[0045] The acquisition module is used to acquire composite phase change materials of energy storage systems in regional energy systems, time-of-use electricity price data of the electricity spot market, and historical electricity consumption data. The time-of-use electricity price data includes day-ahead electricity price data, intraday electricity price data, and real-time electricity price data.

[0046] The determination module is used to determine the thermal storage capacity status of the energy storage unit by detecting the temperature change rate of the composite phase change material during the charging and discharging processes. Based on the day-ahead electricity price data and the thermal storage capacity status, in a multi-timescale coordinated optimization framework, with the objectives of optimizing the total operating cost of the regional energy system and maximizing the renewable energy absorption rate, a day-ahead joint scheduling plan for renewable energy units and energy storage systems is generated.

[0047] The calculation module is used to calculate the elasticity coefficient of electricity price for multiple time periods based on the historical electricity consumption data, and to form an elasticity matrix based on the elasticity coefficient;

[0048] The correction module is used to combine the latest electricity spot market information and regional energy system operation status data to correct the parameters of the elasticity matrix, and determine the load adjustment amount based on the intraday electricity price data and the corrected elasticity matrix.

[0049] The adjustment module is used to calculate the real-time output adjustment commands of the new energy generating units and the energy storage system using a model predictive control algorithm based on the real-time electricity price data and the load adjustment amount, and to synchronously adjust the output level of the new energy generating units and the charging and discharging power of the energy storage system according to the real-time output adjustment commands, so as to achieve joint output optimization of new energy and energy storage.

[0050] Thirdly, this application provides an electronic device, comprising:

[0051] Memory, used to store computer programs;

[0052] A processor is configured to execute the computer program to implement the steps of the multi-timescale new energy-storage joint output optimization method based on spot electricity prices as described in the first aspect above.

[0053] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the multi-timescale new energy-energy storage joint output optimization method based on spot electricity prices as described in the first aspect above.

[0054] This application provides a multi-timescale optimization method for the combined output of new energy and energy storage systems based on spot electricity prices. The method includes: acquiring composite phase change materials of the energy storage system within a regional energy system, time-of-use electricity price data from the electricity spot market, and historical electricity consumption data. The time-of-use electricity price data includes day-ahead price data, intraday price data, and real-time price data. By detecting the temperature change rate of the composite phase change material during charging and discharging processes, the thermal storage capacity status of the energy storage unit is determined. Based on the day-ahead electricity price data and the thermal storage capacity status, within a multi-timescale coordinated optimization framework, with the objectives of optimizing the total operating cost of the regional energy system and maximizing the new energy absorption rate, a method for optimizing the combined output of new energy units and energy storage systems is generated. The system implements a day-ahead joint dispatch plan; based on the historical electricity consumption data, it calculates the elasticity coefficients of electricity prices for multiple time periods, and forms an elasticity matrix based on the elasticity coefficients; combining the latest electricity spot market information and regional energy system operation status data, it corrects the parameters of the elasticity matrix; based on the intraday electricity price data and the corrected elasticity matrix, it determines the load adjustment amount; based on the real-time electricity price data and the load adjustment amount, it uses a model predictive control algorithm to calculate the real-time output adjustment instructions of new energy units and energy storage systems, and synchronously adjusts the output level of new energy units and the charging and discharging power of energy storage systems according to the real-time output adjustment instructions, so as to achieve joint output optimization of new energy and energy storage.

[0055] The technical solution provided in this application has the following beneficial effects:

[0056] This application achieves comprehensive perception of energy storage material characteristics, market signals, and user behavior, providing a data foundation for multi-dimensional collaborative optimization. It establishes a dynamic correlation between the physical state and operational characteristics of energy storage units, improving the accuracy of energy storage status assessment. It couples energy storage physical characteristics with market signals, enhancing the matching degree between day-ahead planning and actual system operation. It quantifies the response patterns of user electricity consumption behavior to electricity prices, providing a basis for precise load control. It enables dynamic updates to the user response model, improving the adaptability of load forecasting and control. It forms a multi-timescale closed-loop optimization, enhancing the system's ability to absorb fluctuating renewable energy sources. It achieves source-storage coordinated control, enhancing system operational stability and power balance capabilities.

[0057] Furthermore, this application establishes a dynamic evaluation mechanism for thermal storage capacity based on the physical properties of the composite phase change material by real-time monitoring the surface temperature change rate during the charging and discharging process; the evaluation results are combined with the day-ahead electricity price signal to construct a dual optimization function that takes into account the system operation objectives under the multi-timescale coordinated optimization framework, thereby generating a day-ahead joint dispatch plan for new energy units and energy storage systems.

[0058] Furthermore, by deeply integrating the physical properties of materials with their operating status, the accuracy of the energy storage system's operating status perception has been improved, the alignment between the day-ahead scheduling plan and the actual operating characteristics of the equipment has been enhanced, and more reliable physical status support has been provided for multi-timescale optimization.

[0059] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 A flowchart illustrating a multi-timescale new energy-energy storage joint output optimization method based on spot electricity prices, provided for embodiments of this application;

[0062] Figure 2 A schematic diagram illustrating a specific implementation of a multi-timescale new energy-energy storage joint output optimization method based on spot electricity prices, provided for an embodiment of this application;

[0063] Figure 3 This is a schematic diagram of a new energy-energy storage joint output optimization system based on spot electricity prices and multiple time scales, provided as an embodiment of this application. Detailed Implementation

[0064] To address the technical bottlenecks in current new energy-energy storage joint optimization, existing predictive control methods based on fixed models face challenges in handling complex operating environments. These methods are insufficient in characterizing the dynamic physical properties of energy storage devices, making it difficult to accurately reflect the state changes during the energy storage and release process of phase change materials. Furthermore, the spatiotemporal differences in user load response to electricity prices are not adequately reflected in the scheduling model, leading to deviations between real-time control commands and the actual system operating state. In addition, the state transitions during multi-timescale optimization are not smooth enough, affecting the stability and control accuracy of the system.

[0065] To address the aforementioned issues, this application proposes a multi-timescale renewable energy-energy storage joint output optimization method based on spot electricity prices. This method accurately assesses the thermal storage capacity of energy storage units by real-time monitoring the temperature change characteristics of composite phase change materials during the charging and discharging process. Combined with multi-timescale electricity price signals, an optimization function incorporating system operation objectives is constructed within a coordinated optimization framework to generate a day-ahead joint scheduling plan for renewable energy units and energy storage systems. Furthermore, based on dynamically corrected elastic matrices and model predictive control algorithms, rolling calculations and execution of real-time output adjustment commands are achieved. This solution effectively improves the accuracy of system operation status perception, the adaptability of load regulation, and the stability of renewable energy consumption through deep integration of energy storage physical characteristics and market signals, precise quantification of user response behavior, and closed-loop optimization across multiple timescales. This fundamentally improves the collaborative control performance of renewable energy-energy storage systems under complex operating environments.

[0066] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0067] The core of this application is to provide a multi-timescale method for optimizing the combined output of new energy and energy storage based on spot electricity prices. A flowchart of one specific implementation method is shown below. Figure 1 As shown, the method includes:

[0068] Step 101: Obtain composite phase change material of energy storage system in regional energy system, time-of-use electricity price data of electricity spot market and historical electricity consumption data. The time-of-use electricity price data includes day-ahead electricity price data, intraday electricity price data and real-time electricity price data.

[0069] In step 101, the regional energy system refers to a localized energy network that includes new energy power generation, energy storage devices, and electricity users. The electricity spot market is the external market environment in which this system participates in electricity trading and obtains real-time electricity prices. The two establish a supply and demand linkage through electricity price signals and electricity trading. Composite phase change materials refer to energy storage media with high latent heat characteristics, whose phase change process is accompanied by heat absorption and release. Time-of-use electricity price data reflects price signals from the electricity spot market at different time dimensions, including day-ahead electricity price data (the electricity price for the next 24 hours determined by the day-ahead market), intraday electricity price data (the electricity price updated on a rolling basis within the trading day), and real-time electricity price data (the dynamic electricity price at the actual operating time). Historical electricity consumption data includes users' electricity consumption records and corresponding electricity price information for specific periods in the past.

[0070] In this embodiment, the identification information of the composite phase change material in the energy storage system is obtained through the data acquisition interface. The electricity market platform is connected simultaneously to extract the electricity price series of three time dimensions: day-ahead, intraday, and real-time. Historical electricity consumption records are retrieved from the user electricity consumption database, and the three types of data are integrated into a standardized input dataset.

[0071] For example, taking the energy system of an industrial park as an example, the composite phase change material type is read as hydrated salt from the energy storage unit configuration file. The day-ahead electricity price data at 96:00 the next day is obtained through the power trading platform API. The electricity price at 08:00 is 0.58 yuan / kWh and at 12:00 is 0.92 yuan / kWh. At the same time, the user electricity consumption records at 15-minute intervals for the past 30 days are obtained by accessing the dispatch center database, forming an initial data set containing material properties, electricity price series and historical load.

[0072] Step 102: By detecting the temperature change rate of the composite phase change material during the charging and discharging processes, the thermal storage capacity status of the energy storage unit is determined. Based on the day-ahead electricity price data and the thermal storage capacity status, a day-ahead joint scheduling plan for the new energy unit and the energy storage system is generated within a multi-timescale coordination and optimization framework, with the objectives of optimizing the total operating cost of the regional energy system and maximizing the new energy absorption rate.

[0073] In step 102, the rate of temperature change refers to the change in surface temperature of the composite phase change material per unit time. An energy storage unit refers to a basic energy storage module constructed using composite phase change materials. Multiple energy storage units are connected in series and parallel to form an energy storage system. The thermal characteristics of the energy storage units determine the thermal storage capacity status of the entire energy storage system. The thermal storage capacity status characterizes the ratio between the current actual thermal storage capacity and the maximum capacity of the energy storage unit. The multi-timescale coordinated optimization framework refers to a collaborative computing structure covering three decision-making levels: day-ahead, intraday, and real-time. New energy units refer to renewable energy power generation equipment with fluctuating characteristics, such as wind power and photovoltaic power generation, which are directly related to the new energy power generation forecast data in this application. Their output level needs to be coordinated and optimized with the energy storage system. The day-ahead joint dispatch plan includes the time series of the planned output value of the new energy units and the planned charge and discharge power value of the energy storage system.

[0074] In this embodiment, a temperature sensor is arranged on the surface of the energy storage unit to continuously monitor the rate of temperature rise during charging and the rate of temperature drop during discharging. The measured rate is compared with the material characteristic curve to calculate the current thermal storage capacity ratio. An objective function is constructed in the optimization framework in combination with the day-ahead electricity price data. The thermal storage capacity status is introduced as a constraint condition for energy storage power. The planned output value of the new energy unit and the planned charging and discharging power value of the energy storage system are obtained by solving mathematical programming.

[0075] For example, eight temperature measuring points were set up on the surface of the energy storage tank in the park. During the charging phase from 10:00 to 10:15, the temperature rose from 45°C to 52°C. The calculated temperature change rate was 0.47°C per minute. Based on the material characteristic curve, the thermal storage capacity was determined to be 82%. The state parameters and the day-ahead electricity price were input into the optimization model, and the solution was obtained that the planned wind power output at 08:00 the next day was 38.5 MW. The energy storage system's charging power during the off-peak period from 02:00 to 05:00 was 12 MW, and the discharge power during the peak period from 10:00 to 12:00 was 15 MW.

[0076] Step 103: Based on the historical electricity consumption data, calculate the elasticity coefficient of electricity price for multiple time periods, and form an elasticity matrix based on the elasticity coefficient.

[0077] In step 103, "multi-period" refers to the three continuous decision-making stages in the electricity spot market: day-ahead, intraday, and real-time. Each stage corresponds to a different electricity price formation mechanism and market clearing time, collectively forming a complete market time-series framework. The elasticity coefficient quantifies the ratio of the rate of change in user electricity consumption to the rate of change in electricity price. The elasticity matrix is ​​a two-dimensional array with time periods as columns and user categories as rows; the element values ​​reflect the price sensitivity of a specific user at different time periods.

[0078] In this embodiment of the application, the electricity price and electricity consumption for each time period are extracted from historical electricity data, the change in electricity price and the change in electricity consumption between adjacent time periods are calculated, and the elasticity coefficient is calculated according to the formula equal to the percentage change in electricity consumption divided by the percentage change in electricity price. The elasticity coefficient between all time periods is calculated for each user category, and the results are arranged in matrix form according to user category.

[0079] For example, based on 30 days of electricity consumption data for three types of users in the park, the elasticity coefficient of large industrial users during the period from 10:00 to 11:00 is calculated. When the electricity price changes from 0.75 yuan / kWh to 0.82 yuan / kWh and the electricity consumption changes from 45.8 MWh to 42.3 MWh, the elasticity coefficient is equal to -3.5 divided by 44.05 divided by 0.07 divided by 0.785, which equals -1.12. Finally, an elasticity matrix with 3 rows and 24 columns is formed.

[0080] Step 104: Combine the latest electricity spot market information and regional energy system operation status data to correct the parameters of the elasticity matrix, and determine the load adjustment amount based on the intraday electricity price data and the corrected elasticity matrix.

[0081] In step 104, the latest electricity spot market information refers to market dynamic data close to the current moment, including but not limited to real-time market signals such as the latest clearing price, trading volume, congestion status, and ancillary service demand. This information is continuously acquired through the real-time data interface of the electricity spot market or the push mechanism of the trading platform. There is an inclusion relationship between "latest electricity spot market information" and "time-of-use electricity price data of the electricity spot market": time-of-use price data is the core price signal in market information, while market information also includes other market factors that affect price formation. Time-of-use price data is the pricing basis of market information, while market information reflects the dynamic changes in electricity prices during real-time trading. Regional energy system operation status data refers to physical measurement data reflecting the real-time operating characteristics of the system, including actual output of new energy sources, actual load values, energy storage status of charge, and grid power flow. This data is collected by measurement devices deployed on power generation equipment, transmission and distribution networks, and user sides, and processed by the data acquisition and monitoring system. Parameter correction refers to the process of adjusting the elasticity matrix value based on real-time information. Load adjustment amount represents the power value that needs to be increased or decreased on the user side.

[0082] In this embodiment, the latest market supply and demand indicators and actual output data of new energy are obtained, and adjustment factors are generated to revise the elasticity matrix twice. The coefficient is adjusted twice based on the market tension. The coefficient is fine-tuned based on the new energy forecast deviation. The load change is calculated based on the revised matrix and the intraday electricity price and allocated according to user category.

[0083] For example, at 10:00, the market supply and demand index is monitored to be 1.2, and the first adjustment factor of 1.2 is generated to correct the elasticity coefficient of large industrial users from -1.12 to -1.344. Subsequently, based on the wind power output deviation of -4.1 MW, the second adjustment factor of 0.9 is generated, and the coefficient is further corrected to -1.21. Combined with the daily electricity price of 0.83 yuan / kWh, the load adjustment of this user is calculated to be -2.9 MW.

[0084] Step 105: Based on the real-time electricity price data and the load adjustment amount, the model predictive control algorithm is used to calculate the real-time output adjustment instructions of the new energy generating units and the energy storage system in a rolling manner. According to the real-time output adjustment instructions, the output level of the new energy generating units and the charging and discharging power of the energy storage system are adjusted synchronously to achieve the joint output optimization of new energy and energy storage.

[0085] In step 105, the output level refers to the actual output state of the new energy generator set relative to its rated capacity during actual operation. Specifically, it is the active power value actually transmitted to the grid by the generator set at a specific moment. This value reflects the real-time working capacity and operating status of the generator set. The real-time output adjustment command represents a control signal that includes the power adjustment value of the new energy generator set and the energy storage power adjustment value.

[0086] In this embodiment, the real-time electricity price sequence and load adjustment amount are input into the model predictive controller to solve an optimization problem that includes power balance constraints, output the unit and energy storage power adjustment sequence for multiple future time periods, and extract the value of the first time period as an immediate instruction to the execution device.

[0087] For example, based on the real-time electricity price sequence for the next four time periods and the total load adjustment of -6.3 MW, the model predictive control solves for the unit adjustment sequence as -1.2 MW, -1.5 MW, -1.8 MW, and -2.1 MW, and the energy storage adjustment sequence as discharging 3.1 MW, discharging 3.4 MW, charging 1.2 MW, and charging 1.5 MW. The first time period command is immediately executed to reduce the wind power output from 38.2 MW to 37.0 MW, and the energy storage discharges at 3.1 MW.

[0088] This method achieves multi-timescale collaborative optimization by deeply coupling the physical properties of energy storage materials with market signals, thereby improving the accuracy of new energy consumption and the stability of system operation, and enhancing the source-storage collaborative control capability.

[0089] To address the insufficient coordination between the physical state and market optimization of energy storage systems, in some embodiments, step 102 involves: determining the thermal storage capacity of the energy storage unit by detecting the temperature change rate of the composite phase change material during the charging and discharging processes; and, based on the day-ahead electricity price data and the thermal storage capacity status, generating a day-ahead joint scheduling plan for the new energy units and the energy storage system within a multi-timescale coordinated optimization framework, with the objectives of optimizing the total operating cost of the regional energy system and maximizing the renewable energy absorption rate. Figure 2 As shown, it includes:

[0090] Step 201: Measure the surface temperature of the composite phase change material during the charging and discharging processes, and record the surface temperature measurement values ​​within a predetermined time window.

[0091] In step 201, the predetermined time window refers to the continuous observation period set for calculating the rate of temperature change. The surface temperature measurement value refers to the sequence of temperature values ​​of the composite phase change material collected by a temperature sensor at a specific location on the surface of the energy storage unit.

[0092] In this embodiment, a temperature sensor array is arranged on the surface of the energy storage unit to continuously collect surface temperature data of the composite phase change material during the charging and discharging phases at a fixed sampling frequency, and the temperature values ​​of multiple consecutive sampling cycles are stored in chronological order as a temperature measurement sequence.

[0093] Step 202: Based on the surface temperature measurement, calculate the first temperature change rate of the composite phase change material during the charging process and the second temperature change rate during the releasing process.

[0094] In step 202, the first temperature change rate characterizes the magnitude of the temperature rise per unit time during the charging process of the composite phase change material, and the second temperature change rate characterizes the magnitude of the temperature drop per unit time during the releasing process.

[0095] In this embodiment of the application, temperature data points of the charging stage are extracted from the stored temperature measurement sequence, and the temperature change slope of the stage is calculated by linear fitting method as the first temperature change rate. The data of the energy release stage are processed in the same way to obtain the second temperature change rate. The two rate values ​​reflect the dynamic characteristics of the material's energy storage and release process.

[0096] Step 203: Establish a multi-timescale coordination and optimization framework that includes the day-ahead phase.

[0097] In step 203, the day-ahead phase, as the basic decision-making level, is responsible for formulating the all-day scheduling plan.

[0098] In this embodiment, a three-layer optimization architecture is constructed, wherein the day-ahead stage receives market forecast data and system status information to generate an initial operation plan covering the entire scheduling cycle, which will serve as a benchmark reference for subsequent adjustment layers.

[0099] Step 204: In the day-ahead phase of the multi-timescale coordinated optimization framework, the thermal storage capacity status is determined based on the first temperature change rate and the second temperature change rate, and an optimization objective function including the total operating cost of the regional energy system and the renewable energy absorption rate is constructed in combination with the day-ahead electricity price data. Based on the optimization objective function, a day-ahead joint dispatch plan is generated.

[0100] In step 204, the objective function is a mathematical expression that includes the economic efficiency of system operation and the efficiency of new energy utilization.

[0101] In this embodiment, the first and second temperature change rates are matched with material characteristic parameters to obtain the current thermal storage capacity percentage of the energy storage unit. A function expression is constructed with the goal of minimizing operating costs and maximizing renewable energy consumption, and the planned output value of the renewable energy unit in each time period within the scheduling cycle is obtained by solving the mathematical optimization algorithm, as well as the planned charging power value and planned discharging power value of the energy storage system in each time period, forming a complete day-ahead joint scheduling plan.

[0102] Here is a specific example:

[0103] During the operation of the energy storage unit in the industrial park's energy system, the thermal storage capacity status detection was initiated at 08:00. First, the surface temperature of the composite phase change material was continuously monitored at eight measuring points on the energy storage tank surface. The temperature data sequence for the charging process within the time window of 08:00-08:15 was recorded as [45.0, 46.2, 47.5, 48.8, 50.1, 51.3, 52.0] degrees Celsius, and the temperature data sequence for the energy release process was recorded as [58.0, 57.2, 56.3, 55.4, 54.5, 53.7, 53.0] degrees Celsius. Based on the surface temperature measurements, the temperature change rate was calculated using a linear regression method. The formula for calculating the first temperature change rate during the charging process is as follows: ,in This represents the first rate of temperature change during the charging process. The temperature change is 52.0 - 45.0 = 7.0 degrees Celsius. Given a time interval of 15 minutes, the first temperature change rate is calculated to be 7.0 / 15 = 0.47 degrees Celsius per minute. The formula for calculating the second temperature change rate during the energy release process is... ,in This represents the second rate of temperature change during the energy release process. Given a temperature change of 53.0 - 58.0 = -5.0 degrees Celsius, the second temperature change rate is calculated to be -5.0 / 15 = -0.33 degrees Celsius per minute. A multi-timescale coordinated optimization framework including the day-ahead phase is established. In the day-ahead phase, the first temperature change rate (0.47) and the second temperature change rate (-0.33) are compared with the material's standard characteristic curve. The standard charging rate range is [0.3, 0.5] degrees Celsius per minute, and the standard releasing rate range is [-0.4, -0.2] degrees Celsius per minute. Both measured rates fall within the standard range. Based on the degree of rate deviation, the thermal storage capacity is calculated to be 82%. Combining the day-ahead electricity price data (0.58 yuan per kilowatt-hour at 08:00 and 0.92 yuan per kilowatt-hour at 12:00), an optimization objective function is constructed. ,in for Electricity purchased outside the region during a given period is measured in megawatt-hours. for The unit of electricity price for each time period is yuan per kilowatt-hour. for The unit for wind curtailment during a given period is megawatt-hour (MWH). Based on this optimization objective function, the planned output of the renewable energy generating units at 08:00 the following day is 38.5 MW, the planned charging power of the energy storage system at 02:00-05:00 is 12 MW, and the planned discharging power at 10:00-12:00 is 15 MW. These values ​​together constitute the day-ahead joint dispatch plan.

[0104] In this embodiment, by real-time monitoring of the dynamic temperature characteristics of energy storage materials and incorporating multi-timescale optimization decision-making, the accuracy of energy storage status assessment is improved, the matching degree between day-ahead plans and actual equipment operating status is enhanced, and an effective guarantee is provided for the stable operation of the system.

[0105] To further improve the matching accuracy between energy storage status assessment and scheduling plans, in some embodiments, step 204 involves: determining the thermal storage capacity status based on the first temperature change rate and the second temperature change rate, and constructing an optimized objective function that includes the total operating cost of the regional energy system and the renewable energy absorption rate, in conjunction with the day-ahead electricity price data; and generating a day-ahead joint scheduling plan based on the optimized objective function, including:

[0106] Step 301: Compare the first temperature change rate with a preset first reference rate range, compare the second temperature change rate with a preset second reference rate range, and determine the thermal storage capacity status of the energy storage unit based on the comparison results, wherein the upper and lower limits of the second reference rate range are numerically smaller than the upper and lower limits of the first reference rate range.

[0107] In step 301, the first reference rate range refers to the reasonable range of temperature change rate of the composite phase change material during normal charging, and the second reference rate range refers to the reasonable range of temperature change rate during normal energy release.

[0108] In this embodiment of the application, the measured first temperature change rate is compared with the preset standard rate range of the charging process, and the second temperature change rate is compared with the standard rate range of the releasing process. The current thermal storage capacity ratio of the energy storage unit is calculated based on the relative positions of the two rate values ​​within their respective standard ranges.

[0109] Step 302: Based on the thermal storage capacity status, determine the maximum charging power limit and the maximum discharging power limit that the energy storage unit can provide during the scheduling cycle.

[0110] In step 302, the maximum charging power limit refers to the maximum input power value allowed by the energy storage unit within the scheduling cycle, and the maximum discharging power limit refers to the maximum output power value allowed.

[0111] In this embodiment of the application, based on the determined thermal storage capacity state value, a preset thermal storage capacity and power correspondence table is queried to obtain the maximum charging power and maximum discharging power values ​​of the energy storage unit corresponding to that state.

[0112] Step 303: Using the maximum charging power limit and the maximum discharging power limit as operating constraints of the energy storage unit, and combining the day-ahead electricity price data, construct an optimization objective function that includes the total operating cost of the regional energy system and the renewable energy absorption rate.

[0113] In step 303, the running constraints refer to the boundary conditions in the optimization model that restrict the power variation of the energy storage unit.

[0114] In this embodiment, the maximum charging power limit and the maximum discharging power limit are used as upper and lower limits of energy storage power constraints, and an objective function expression including the electricity purchase cost and the renewable energy curtailment penalty is constructed in combination with the day-ahead electricity price data.

[0115] Step 304: Solve the optimization objective function to obtain the planned output value of the new energy unit in each time period of the scheduling cycle, and the planned charging power value and planned discharging power value of the energy storage system in each time period of the scheduling cycle.

[0116] In step 304, the planned output value refers to the power generation value pre-arranged by the new energy unit in each time period, and the planned charging power value and planned discharging power value refer to the charging and discharging power values ​​pre-arranged by the energy storage system in each time period.

[0117] In this embodiment of the application, a mathematical programming algorithm is used to solve the constrained optimization objective function, and output the planned power generation value of the new energy unit in each time period within the scheduling cycle, as well as the planned charging power value and planned discharging power value of the energy storage system in each time period.

[0118] Step 305: Based on the planned output value, the planned charging power value, and the planned discharging power value, form a day-ahead joint scheduling plan.

[0119] In this embodiment, the planned power output, planned charging power, and planned discharging power values ​​for each time period are integrated and arranged in chronological order to generate a scheduling plan document with a unified format. Specifically, the three types of data—the planned power output of the new energy generating units for each time period, the planned charging power of the energy storage system for each time period, and the planned discharging power of the energy storage system—are integrated and arranged in chronological order to generate a scheduling instruction sequence with a unified time coordinate. A specific example is as follows: Divide 24 hours into 96 15-minute time slots. Record the planned output value of 38.5 MW, the planned charging power value of 0 MW, and the planned discharging power value of 0 MW at 08:00. Record the planned output value of 42.3 MW, the planned charging power value of 0 MW, and the planned discharging power value of 15 MW at 10:00. Record the planned output value of 35.8 MW, the planned charging power value of 0 MW, and the planned discharging power value of 8 MW at 14:00. And so on, recording the three types of values ​​for all 96 time slots. Finally, integrate them into a standardized scheduling plan table that includes timestamps, new energy output instructions, energy storage charging instructions, and energy storage discharging instructions.

[0120] Here is a specific example:

[0121] During the operation of the energy storage unit in the industrial park's energy system, based on the measured first temperature change rate of 0.47 degrees Celsius per minute and the second temperature change rate of -0.33 degrees Celsius per minute, the first temperature change rate is compared with a first reference rate range of 0.3 to 0.5 degrees Celsius per minute, and the second temperature change rate is compared with a second reference rate range of -0.4 to -0.2 degrees Celsius per minute. The upper and lower limits of the first reference rate range (0.3 and 0.5) are numerically greater than the absolute values ​​of the upper and lower limits of the second reference rate range (-0.4 and -0.2). Based on the comparison results, a state calculation formula is used. ,in The percentage is the state of thermal storage capacity. The first temperature change rate is 0.47 degrees Celsius per minute. The median of the first reference rate range is 0.4 degrees Celsius per minute. The first reference rate range width is 0.2 degrees Celsius per minute. The second rate of temperature change is -0.33 degrees Celsius per minute. The second reference rate range is 0.3 degrees Celsius per minute minus the median. Assuming the second reference rate range width is 0.2 degrees Celsius per minute, substituting this into the calculation yields... The thermal storage capacity status of the energy storage unit is determined as follows: Based on the state-to-power mapping table of the thermal storage capacity, when the state value is in the 80-85% range, the corresponding maximum charging power limit is 15 MW and the maximum discharging power limit is 18 MW. Using the maximum charging power limit of 15 MW and the maximum discharging power limit of 18 MW as operating constraints for the energy storage unit, and combining this with the day-ahead electricity price data (08:00: 0.58 yuan / kWh and 12:00: 0.92 yuan / kWh), an optimization objective function is constructed. The objective function was solved using a linear programming algorithm, yielding a planned output of 38.5 MW for the new energy generating units at 08:00 within the scheduling cycle, a planned charging power of 12 MW for the energy storage system from 02:00 to 05:00, and a planned discharging power of 15 MW from 10:00 to 12:00. Based on these planned output, charging, and discharging power values, a day-ahead joint scheduling plan containing complete operating arrangements for 96 time periods was formed by integrating them in chronological order.

[0122] In this embodiment of the application, the precise energy storage status assessment and dynamic setting of power limits enhance the fit between the optimization model and the actual equipment operating status, thereby improving the feasibility and execution effect of the scheduling plan.

[0123] To further improve the accuracy of the optimization solution and the rationality of the scheduling plan, in some embodiments, step 304: solving the optimization objective function to obtain the planned output value of the new energy unit in each time period within the scheduling cycle, and the planned charging power value and planned discharging power value of the energy storage system in each time period within the scheduling cycle, includes:

[0124] Step 401: Combine the operating cost item and the new energy consumption item in the optimization objective function into a comprehensive optimization index.

[0125] In step 401, the operating cost item refers to the sum of the electricity purchase cost and the power generation cost generated during the system operation process, the new energy consumption item refers to the power curtailment penalty item set to promote the utilization of new energy, and the comprehensive optimization index refers to the unified evaluation index after integrating the two types of objectives.

[0126] In this embodiment of the application, a mathematical expression for operating costs, including electricity purchase costs and unit power generation costs, is extracted from the objective function. At the same time, a penalty term expression reflecting the curtailment of renewable energy is extracted. The two expressions are then combined into a single comprehensive optimization index through a linear combination.

[0127] Step 402: Based on the day-ahead electricity price data, determine the electricity price weighting coefficient for each time period, and perform weighted processing on the comprehensive optimization index based on the electricity price weighting coefficient.

[0128] In step 402, the electricity price weighting coefficient refers to the weight ratio determined based on the electricity price level of each time period, and the weighting process refers to adjusting the importance of the corresponding time period component in the optimization index using the weighting coefficient. "Weighting the comprehensive optimization index based on the electricity price weighting coefficient" means that the mathematical expression of the optimization objective function will change. Specifically, it involves introducing weighting coefficients related to the electricity price of each time period on top of the original comprehensive optimization index. These coefficients act as multipliers, combined with the cost or absorption term of the corresponding time period, thereby changing the contribution ratio of each item in the original objective function and forming a new objective function expression weighted by the electricity price.

[0129] In this embodiment of the application, the ratio of electricity price to average electricity price for each time period is calculated based on the day-ahead electricity price data and used as the electricity price weighting coefficient. These coefficients are then used to weight and amplify or reduce the cost items and penalty items for the corresponding time periods in the comprehensive optimization index.

[0130] Step 403: Using the predicted output range of the new energy unit as the unit output constraint, the optimal solution of the weighted comprehensive optimization index is obtained through iterative calculation under the operating constraint and the unit output constraint.

[0131] In step 403, the predicted output range of the new energy unit refers to the interval formed by the minimum and maximum output values ​​that the new energy unit may reach in a specific future period, calculated based on meteorological forecast data and unit performance characteristics. This range is derived by analyzing the correspondence between historical power data and meteorological conditions, combined with the unit's rated capacity. The termination condition of the iteration is that the calculation process stops when the difference between the comprehensive optimization index values ​​obtained from two consecutive iterations is less than a preset convergence threshold, or when the number of iterations reaches the preset maximum allowable number of iterations. The optimal solution refers to the value of the decision variable that makes the weighted comprehensive optimization index reach its best value.

[0132] In this embodiment, the predicted output range of the new energy unit is used as the output constraint of the optimization model. Combined with the operating constraints of the energy storage system, an iterative algorithm is used to repeatedly calculate the weighted comprehensive optimization index until the optimal objective function value that satisfies all constraints is found.

[0133] Step 404: Based on the optimal solution, obtain the planned output value of the new energy unit in each time period of the scheduling cycle, and the planned charging power value and planned discharging power value of the energy storage system in each time period of the scheduling cycle.

[0134] In this embodiment of the application, the output decision variable values ​​of the new energy unit in each time period are extracted from the optimal solution obtained by iterative calculation as the planned output value, and the charging power and discharging power decision variable values ​​of the energy storage system in each time period are extracted as the planned charging power value and planned discharging power value.

[0135] Here is a specific example:

[0136] In the process of optimizing the energy system of the industrial park, based on the established optimization objective function... The weighting coefficients for electricity prices in each time period are calculated based on the day-ahead electricity price data, using the formula... ,in for The time-based electricity price weighting coefficient is dimensionless. for The unit of electricity price for each time period is yuan per kilowatt-hour. With an average daily electricity price of 0.68 yuan per kilowatt-hour, the calculated weighting coefficient for the 08:00 time period is 0.58 divided by 0.68, which equals 0.85, and the weighting coefficient for the 12:00 time period is 0.92 divided by 0.68, which equals 1.35.

[0137] Based on these electricity price weighting coefficients, the comprehensive optimization index is weighted to obtain the weighted comprehensive optimization index. ,in The unit for the weighted comprehensive optimization index is element. The predicted output range of the new energy generating units, from 0 to 50 MW, is used as the unit output constraint. Under the constraints of energy storage operation (maximum charging power limit of 15 MW, maximum discharging power limit of 18 MW) and unit output constraint, iterative calculations are performed using the simplex method. The convergence condition is set to two consecutive iterations. With a relative change of less than 0.1 percentage points, the optimal solution for the weighted comprehensive optimization index was obtained after 12 iterations, amounting to 28,650 yuan. Based on this optimal solution, decision variable values ​​were extracted, yielding the planned output of the new energy generating units at 08:00 within the dispatch cycle: 38.5 MW; the planned charging power of the energy storage system at 02:00 to 05:00: 12 MW; and the planned discharging power at 10:00 to 12:00: 15 MW. These values ​​constitute the core parameters of the day-ahead joint dispatch plan.

[0138] In the embodiments of this application, by integrating multiple objectives and weighting optimization, the responsiveness of the optimization model to electricity price signals is enhanced, and the feasibility and economy of the scheduling plan under complex constraints are improved.

[0139] To accurately quantify user electricity consumption behavior characteristics to support precise load regulation, in some embodiments, step 103: calculating the elasticity coefficient of electricity price for multiple time periods based on the historical electricity consumption data, and forming an elasticity matrix based on the elasticity coefficient, includes:

[0140] Step 501: Extract historical electricity price data and corresponding historical electricity consumption data for each predetermined time period from the historical electricity consumption data.

[0141] In step 501, the predetermined time period refers to the electricity price settlement cycle in the electricity spot market divided according to fixed time intervals. Specifically, this includes dividing a 24-hour day into multiple consecutive time intervals of equal or unequal length, such as dividing a day into 96 15-minute intervals, 24 1-hour intervals, or multiple electricity price intervals of different lengths based on peak-valley characteristics. Historical electricity price data refers to the actual electricity price records for each time period on a specific past date, and historical electricity consumption data refers to the total electricity consumption records of users for the corresponding time period.

[0142] In this embodiment of the application, the electricity price values ​​and corresponding user electricity consumption values ​​for each time period of multiple historical dates are extracted from the historical database of the electricity information system at fixed daily time intervals to form a structured dataset containing timestamps, electricity prices and electricity consumption.

[0143] Step 502: For two adjacent time periods, based on the historical electricity price data and the corresponding historical electricity consumption data, calculate the electricity price difference between the first time period and the second time period, and use the electricity price difference as the change in electricity price during the time period. Also calculate the difference in user electricity consumption between the first time period and the second time period, and use the difference in user electricity consumption as the change in electricity consumption during the time period.

[0144] In step 502, the first time period refers to the earlier time period in chronological order, and the second time period refers to the immediately following time period. For example, when calculating the elasticity coefficient between two adjacent time periods, 10:00 and 11:00, 10:00 is the first time period (the earlier time period), and 11:00 is the second time period (the later time period). The change in electricity price between time periods refers to the price difference between two adjacent time periods, and the change in electricity consumption between time periods refers to the difference in electricity consumption per user between two adjacent time periods.

[0145] In this embodiment of the application, for each pair of adjacent time periods, the difference between the electricity price of the next time period and the electricity price of the previous time period is calculated as the change in electricity price of the time period, and the difference between the electricity consumption of the next time period and the electricity consumption of the previous time period is calculated as the change in electricity consumption of the time period.

[0146] Step 503: Calculate the elasticity coefficient of electricity consumption to electricity price for each type of user in different time periods based on the change in electricity price and the change in electricity consumption during the time period.

[0147] In step 503, based on historical data such as electricity consumption characteristics, load size, and price sensitivity, all users are divided into different categories through cluster analysis or predefined classification rules. Therefore, although steps 501-502 use overall "user electricity consumption data," this data has already been classified, stored, and statistically analyzed according to predefined user categories during collection, allowing subsequent calculations to directly calculate the elasticity coefficient for each "category" of users. From the beginning of the steps, "user" refers to the categorized users, not the undifferentiated overall user population.

[0148] In the embodiments of this application, the elasticity coefficient between each adjacent time period is calculated based on the change in electricity price and the change in electricity consumption for each pair of adjacent time periods, combined with the benchmark electricity price and benchmark electricity consumption, according to the elasticity coefficient definition formula.

[0149] Step 504: Arrange multiple elasticity coefficients belonging to the same type of user in the order of time periods, and combine them to form the elasticity vector of the corresponding user.

[0150] In step 504, the elasticity vector refers to a one-dimensional array formed by arranging the elasticity coefficients of the same type of user in all adjacent time periods in chronological order.

[0151] In this embodiment of the application, the calculated elasticity coefficient values ​​of a certain type of user between each adjacent time period are arranged and combined according to the order of the time periods to form a complete elasticity feature sequence of that type of user.

[0152] Step 505: Combine the elasticity vectors of all user categories to construct an elasticity matrix that reflects the relationship between user electricity consumption behavior and electricity price changes in the entire regional energy system.

[0153] In this embodiment, the elastic vectors of different user categories are used as row vectors of a matrix and arranged and combined according to a preset user category order to construct an elastic matrix that reflects the overall user electricity consumption behavior of the system.

[0154] Here is a specific example:

[0155] In constructing the energy system resilience matrix for the industrial park, based on the acquired historical electricity consumption data from the past 30 days, the historical electricity price data and corresponding historical electricity consumption data for 24 hourly time slots daily are first extracted from the electricity information system for three types of users: large industrial users, commercial users, and residential users. For two adjacent time slots, 10:00 and 11:00, the price difference between 10:00 and 11:00 is calculated based on the historical electricity price data. This price difference is used as the change in electricity price for the time slot; specifically, the price at 11:00 is 0.82 yuan per kWh minus the price at 10:00 is 0.75 yuan per kWh, resulting in a change in electricity price of 0.07 yuan per kWh. Simultaneously, the difference in user electricity consumption between the first and second time slots is calculated, and this difference is used as the change in user electricity consumption for the time slot; specifically, the electricity consumption at 11:00 is 42.3 MWh minus the electricity consumption at 10:00 is 45.8 MWh, resulting in a change in electricity consumption of -3.5 MWh. Based on the changes in electricity price and electricity consumption for the time slots, the resilience coefficient calculation formula is used. ,in The elasticity coefficient is dimensionless. The change in electricity consumption over a given period is -3.5 megawatt-hours. The baseline electricity consumption is 44.05 MWh, which is the average of the electricity consumption at 10:00 and 11:00, which is 45.8 MWh plus 42.3 MWh divided by 2. The change in electricity price over a given period is 0.07 yuan per kilowatt-hour. Using the benchmark electricity price of 0.785 yuan per kilowatt-hour (i.e., the average of the prices at 10:00 and 11:00), plus 0.82 and divided by 2, the elasticity coefficient for large industrial users in this adjacent time period is calculated as -3.5 divided by 44.05 divided by 0.07 divided by 0.785, which equals -1.12. The elasticity coefficients for all 23 adjacent time periods belonging to large industrial users are arranged in the order of 0-1:00 and 1-2:00, 1-2:00 and 2-3:00, up to 22-23:00 and 23-24:00, forming the user's elasticity vector from -0.15 to -0.23 to -0.08, down to -1.12. The same method is used to calculate the elasticity vectors for commercial and residential users. Combining the elasticity vectors of all user categories, a 3x23 elasticity matrix is ​​constructed, where the first row corresponds to large industrial users, the second row to commercial users, and the third row to residential users. This matrix comprehensively reflects the response characteristics of different user categories in the entire regional energy system to electricity price changes across different time periods.

[0156] In this embodiment of the application, an elastic matrix reflecting electricity price sensitivity is constructed through systematic extraction and quantitative analysis of user electricity consumption behavior characteristics, providing a reliable data foundation for subsequent precise load control.

[0157] To dynamically adapt to user response characteristics and achieve precise load regulation, in some embodiments, step 104: combining the latest electricity spot market information and regional energy system operation status data to correct the parameters of the elasticity matrix, and determining the load adjustment amount based on the intraday electricity price data and the corrected elasticity matrix, includes:

[0158] Step 601: Based on the supply and demand tension index in the latest electricity spot market information, generate a first adjustment factor, and based on the first adjustment factor, make the first correction to the elasticity vector of the corresponding time period in the elasticity matrix.

[0159] In step 601, the supply-demand tension index in the latest electricity spot market information is a comprehensive parameter derived from analyzing the supply-demand ratio, reserve capacity, and line congestion in real-time market data. It reflects the current balance between electricity supply and demand. The first adjustment factor refers to the elasticity coefficient correction multiplier generated based on market supply and demand conditions.

[0160] In this embodiment of the application, the supply and demand tension index value is extracted from the real-time information of the electricity spot market, and a first adjustment factor is generated based on the comparison result of the value and the benchmark value. The elasticity coefficient of the current period in the elasticity matrix is ​​corrected by multiplication using the factor.

[0161] Step 602: Based on the deviation between the real-time output and the predicted output of the new energy source in the operating status data, generate a second adjustment factor. Based on the second adjustment factor, perform a second correction on the elastic vector after the first correction to obtain the corrected elastic matrix.

[0162] In step 602, the deviation between the real-time output and predicted output of new energy sources in the operating status data is a difference obtained by comparing the actual measured power generation of the new energy power plant with the pre-predicted power generation. It represents the degree of deviation between the actual performance of new energy power generation and the expected plan. The second adjustment factor refers to the elasticity coefficient secondary correction multiplier generated based on the new energy output deviation.

[0163] In this embodiment, the deviation between the real-time output and the predicted output of the new energy source is obtained from the system operation status data. A second adjustment factor is generated based on the magnitude and direction of the deviation. The elastic coefficient, which has been corrected in the first step, is then multiplied and corrected again to obtain the final corrected elastic matrix.

[0164] Step 603: Based on the intraday electricity price data and the corrected elasticity matrix, calculate the amount of user load change required to achieve system power balance during the current intraday period.

[0165] In step 603, the user load change refers to the total load power value that needs to be adjusted to maintain system power balance.

[0166] In this embodiment of the application, based on the intraday electricity price data of the current period and the corrected elasticity matrix, the total load change of each user category required to achieve system power balance is calculated according to the load response model.

[0167] Step 604: Allocate the user load change according to user category and determine the load adjustment amount for each user category.

[0168] In step 604, the user category is a classification based on the user's electricity consumption characteristics, load size, and price sensitivity. This means that user groups with similar electricity consumption behavior patterns are grouped into the same type for unified management.

[0169] In this embodiment of the application, the calculated user load change is proportionally allocated according to the proportion of each user category in the total load and the size of its elasticity coefficient, and the specific load adjustment value for each user category is determined.

[0170] Here is a specific example:

[0171] During the real-time operation of the industrial park's energy system at 10:00 AM, based on the latest electricity spot market information, the supply and demand tension index of 1.2, which is greater than the benchmark value of 1.0, indicates a tight market supply. Therefore, a first adjustment factor of 1.2 is generated. Based on this first adjustment factor, the elasticity vector in the elasticity matrix for the period from 10:00 AM to 11:00 AM is corrected for the first time. The elasticity coefficient for large industrial users is corrected from -1.12 to -1.12 multiplied by 1.2, which equals -1.344; the elasticity coefficient for commercial users is corrected from -0.85 to -0.85 multiplied by 1.2, which equals -1.02; and the elasticity coefficient for residential users is corrected from -0.45 to -0.45 multiplied by 1.2, which equals -0.54. Based on the deviation of -4.1 MW between the real-time renewable energy output of 38.2 MW and the predicted output of 42.3 MW in the operational status data, this negative deviation indicates that the actual renewable energy generation is lower than expected. A second adjustment factor of 0.9 is generated. Based on this second adjustment factor, the elasticity vector, after the first correction, is further corrected. The elasticity coefficient for large industrial users is corrected from -1.344 to -1.344 multiplied by 0.9, which equals -1.21; the elasticity coefficient for commercial users is corrected from -1.02 to -1.02 multiplied by 0.9, which equals -0.92; and the elasticity coefficient for residential users is corrected from -0.54 to -0.54 multiplied by 0.9, which equals -0.49. This yields the corrected elasticity matrix. Based on the intraday electricity price data (actual electricity price of 0.83 yuan per kilowatt-hour at 10:00 AM) and the corrected elasticity matrix, the formula is used... Calculate the change in user load, where The unit for user load variation is megawatt. for The user elasticity coefficient is dimensionless. The change in electricity price of 0.83 minus the benchmark electricity price of 0.78 equals 0.05 yuan per kilowatt-hour. for The user base load is in megawatts. Substituting the base loads of large industrial users (48 MW), commercial users (36 MW), and residential users (24 MW), the calculation yields... This equals -1.21 multiplied by 0.05 multiplied by 48 plus -0.92 multiplied by 0.05 multiplied by 36 plus -0.49 multiplied by 0.05 multiplied by 24, which equals -2.904 plus -1.656 plus -0.588, which equals -6.3 MW. The -6.3 MW user load change is allocated according to user category. Based on the load percentages of each user (44% industrial users, 33% commercial users, 23% residential users) and the elasticity coefficient, the load adjustment amounts are determined as follows: -2.9 MW for large industrial users, -2.2 MW for commercial users, and -1.2 MW for residential users. These load adjustments will be used for subsequent real-time optimization control.

[0172] In this embodiment, a dual correction mechanism based on market status and system operating status is used to achieve dynamic optimization of elastic parameters, thereby improving the accuracy and adaptability of load adjustment calculation.

[0173] To achieve real-time coordinated control of new energy and energy storage, in some embodiments, step 105: based on the real-time electricity price data and the load adjustment amount, using a model predictive control algorithm to calculate the real-time output adjustment command of the new energy unit and the energy storage system, includes:

[0174] Step 701: Based on the real-time electricity price data and the load adjustment amount, construct a real-time optimization problem that includes system power balance constraints.

[0175] In step 701, the system power balance constraint refers to the physical condition that, in the real-time operation of the power system, the total output of the generation side and the total load (including network losses) of the consumption side must remain equal at any time. Specifically, mathematically, it is expressed as the algebraic sum of the actual output of the new energy units, the actual charging and discharging power of the energy storage system (discharging is positive and charging is negative), the power purchased from the main grid, and the user load demand (including load adjustment) being zero.

[0176] In this embodiment of the application, a mathematical model for a real-time optimization problem is constructed based on real-time electricity price data and a determined load adjustment amount, with operational economy as the optimization objective and system power balance as the core constraint.

[0177] Step 702: Using the model predictive control algorithm, the real-time optimization problem is solved in a rolling manner to obtain the planned output adjustment sequence of the new energy unit and the planned power adjustment sequence of the energy storage system for several future periods.

[0178] In step 702, the planned output adjustment sequence refers to the sequence of active power adjustment of the new energy generating units in multiple future time periods, and the planned power adjustment sequence refers to the sequence of charging and discharging power adjustment of the energy storage system in multiple future time periods.

[0179] In this embodiment, a model predictive control algorithm is used to resolve the real-time optimization problem at the beginning of each control cycle to obtain the power adjustment sequence of the new energy unit and the energy storage system in the future several time periods.

[0180] Step 703: Extract the adjustment amount of the first time period from the planned output adjustment sequence as the real-time output adjustment command of the new energy unit, and extract the adjustment amount of the first time period from the planned power adjustment sequence as the real-time power adjustment command of the energy storage system.

[0181] In step 703, the real-time output adjustment instruction refers to the amount of power adjustment of the new energy unit that needs to be executed in the current control cycle, and the real-time power adjustment instruction refers to the amount of power adjustment of the energy storage system that needs to be executed in the current control cycle.

[0182] In this embodiment of the application, the value of the first time period is taken from the solved planned output adjustment sequence as the real-time output adjustment command of the new energy unit, and the value of the first time period is taken from the planned power adjustment sequence as the real-time power adjustment command of the energy storage system.

[0183] Step 704: Combine the real-time output adjustment command of the new energy unit and the real-time power adjustment command of the energy storage system to form a real-time output adjustment command.

[0184] In this embodiment, the real-time output adjustment command of the new energy unit and the real-time power adjustment command of the energy storage system are combined and encapsulated to form a real-time output adjustment command package that can be directly sent to the execution equipment.

[0185] Here is a specific example:

[0186] During the real-time control phase of the industrial park's energy system at 10:00 AM, based on the real-time electricity price data for the next four time periods (0.83 yuan / kWh, 0.84 yuan / kWh, 0.85 yuan / kWh, 0.86 yuan / kWh) and the total load adjustment of -6.3 MW, a real-time optimization problem with system power balance constraints is constructed. The objective function of this problem is: ,in for Time-of-use electricity price weighting factor for The unit for adjusting wind power output during a given time period is megawatts. for Time-of-use energy storage regulation cost coefficient for The unit for adjusting the energy storage capacity during a given time period is megawatts (MW), and the constraints include system power balance constraints. ,in for The time-period load adjustment amount, and the energy storage power constraint of -15 MW less than or equal to Wind power output limit: less than or equal to 18 MW, with a minimum output limit of -5 MW. The power output is less than or equal to 5 MW. A model predictive control algorithm is used to solve this real-time optimization problem using a rolling solution. A quadratic programming method is used to calculate the planned output adjustment sequences for the renewable energy generating units over the next four time periods: -1.2 MW, -1.5 MW, -1.8 MW, and -2.1 MW. The planned power adjustment sequences for the energy storage system over the next four time periods are: discharge 3.1 MW, discharge 3.4 MW, charging 1.2 MW, and charging 1.5 MW. The adjustment amount of -1.2 MW for the first time period is extracted from the planned output adjustment sequences as the immediate output adjustment command for the renewable energy generating units, and the adjustment amount of 3.1 MW for discharge is extracted from the planned power adjustment sequences as the immediate power adjustment command for the energy storage system. The real-time output adjustment command of -1.2 MW for the new energy generator unit and the real-time power adjustment command of 3.1 MW for the energy storage system are combined to form a real-time output adjustment command. This command explicitly requires adjusting the current output of the wind turbine unit from 38.2 MW to 37.0 MW, while controlling the energy storage system to discharge at a power of 3.1 MW. These commands are issued to the corresponding equipment through the control system for execution, and the next round of rolling optimization calculation begins at 10:15.

[0187] In this embodiment, the rolling optimization mechanism of model predictive control enables real-time coordinated regulation of new energy sources and energy storage, effectively improving the stability and economy of system operation.

[0188] Figure 3 A schematic diagram of a multi-timescale new energy-energy storage joint output optimization system based on spot electricity prices is provided for embodiments of this application. The specific implementation method is described as follows:

[0189] The acquisition module 31 is used to acquire composite phase change materials of energy storage systems in regional energy systems, time-of-use electricity price data of the electricity spot market, and historical electricity consumption data. The time-of-use electricity price data includes day-ahead electricity price data, intraday electricity price data, and real-time electricity price data.

[0190] The determination module 32 is used to determine the thermal storage capacity status of the energy storage unit by detecting the temperature change rate of the composite phase change material during the charging and discharging processes. Based on the day-ahead electricity price data and the thermal storage capacity status, in a multi-timescale coordinated optimization framework, with the objectives of optimizing the total operating cost of the regional energy system and maximizing the renewable energy absorption rate, a day-ahead joint scheduling plan for renewable energy units and energy storage systems is generated.

[0191] The calculation module 33 is used to calculate the elasticity coefficient of electricity price for multiple time periods based on the historical electricity consumption data, and to form an elasticity matrix based on the elasticity coefficient.

[0192] The correction module 34 is used to combine the latest electricity spot market information and regional energy system operation status data to correct the parameters of the elasticity matrix, and determine the load adjustment amount based on the intraday electricity price data and the corrected elasticity matrix.

[0193] The adjustment module 35 is used to calculate the real-time output adjustment command of the new energy unit and the energy storage system based on the real-time electricity price data and the load adjustment amount using the model predictive control algorithm, and to synchronously adjust the output level of the new energy unit and the charging and discharging power of the energy storage system according to the real-time output adjustment command, so as to achieve the joint output optimization of new energy and energy storage.

[0194] The new energy-energy storage joint output optimization system based on spot electricity prices and multi-timescale is used to implement the aforementioned new energy-energy storage joint output optimization method based on spot electricity prices and multi-timescale. Therefore, the specific implementation of the new energy-energy storage joint output optimization system based on spot electricity prices and multi-timescale is described in the previous section on the implementation of the new energy-energy storage joint output optimization method based on spot electricity prices and multi-timescale. The specific implementation can be referred to the description of the corresponding implementation of each part, and will not be repeated here.

[0195] This application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the above-described method for optimizing the combined output of new energy and energy storage based on spot electricity prices across multiple time scales.

[0196] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described methods for optimizing the combined output of new energy and energy storage based on spot electricity prices across multiple time scales.

[0197] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0198] The embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the embodiments of the new energy-storage joint output optimization method based on spot electricity prices and multiple time scales.

[0199] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0200] The foregoing has provided a detailed description of a multi-timescale new energy-energy storage joint output optimization method, system, electronic equipment, and storage medium based on spot electricity prices, as provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A multi-timescale new energy-energy storage joint output optimization method based on spot electricity prices, characterized in that, include: Acquire composite phase change materials for energy storage systems in regional energy systems, time-of-use electricity price data from the electricity spot market, and historical electricity consumption data. The time-of-use electricity price data includes day-ahead electricity price data, intraday electricity price data, and real-time electricity price data. By detecting the temperature change rate of the composite phase change material during the charging and discharging processes, the thermal storage capacity status of the energy storage unit is determined. Based on the day-ahead electricity price data and the thermal storage capacity status, a day-ahead joint scheduling plan for new energy units and energy storage systems is generated within a multi-timescale coordination and optimization framework, with the objectives of optimizing the total operating cost of the regional energy system and maximizing the new energy absorption rate. Based on the historical electricity consumption data, the elasticity coefficients of electricity prices for different time periods are calculated, and an elasticity matrix is ​​formed based on the elasticity coefficients. By combining the latest electricity spot market information and regional energy system operation status data, the parameters of the elasticity matrix are corrected, and the load adjustment amount is determined based on the intraday electricity price data and the corrected elasticity matrix. Based on the real-time electricity price data and the load adjustment amount, the model predictive control algorithm is used to calculate the real-time output adjustment instructions of the new energy generating units and the energy storage system in a rolling manner. According to the real-time output adjustment instructions, the output level of the new energy generating units and the charging and discharging power of the energy storage system are adjusted synchronously to achieve the joint output optimization of new energy and energy storage. The process involves determining the thermal storage capacity of the energy storage unit by detecting the temperature change rate of the composite phase change material during the charging and discharging processes. Based on the day-ahead electricity price data and the thermal storage capacity status, and within a multi-timescale coordinated optimization framework, a day-ahead joint dispatch plan for the new energy unit and the energy storage system is generated with the objectives of optimizing the total operating cost of the regional energy system and maximizing the renewable energy absorption rate. This plan includes: The surface temperature of the composite phase change material is measured during the charging and discharging processes, and the surface temperature measurement values ​​are recorded within a predetermined time window. Based on the surface temperature measurement, the first temperature change rate of the composite phase change material during the charging process and the second temperature change rate during the releasing process are calculated. Establish a multi-timescale coordination and optimization framework that includes the day-ahead phase; In the day-ahead phase of the multi-timescale coordinated optimization framework, the thermal storage capacity status is determined based on the first temperature change rate and the second temperature change rate. Combined with the day-ahead electricity price data, an optimization objective function is constructed that includes the total operating cost of the regional energy system and the renewable energy absorption rate. Based on the optimization objective function, a day-ahead joint dispatch plan is generated. The elasticity coefficients of electricity prices for different time periods are calculated based on the historical electricity consumption data. An elasticity matrix is ​​then formed based on these elasticity coefficients, including: Extract historical electricity price data and corresponding historical electricity consumption data for each predetermined time period from multiple historical days from the historical electricity consumption data; For two adjacent time periods, based on the historical electricity price data and the corresponding historical electricity consumption data, the electricity price difference between the first time period and the second time period is calculated, and the electricity price difference is used as the change in electricity price during the time period. The user electricity consumption difference between the first time period and the second time period is also calculated, and the user electricity consumption difference is used as the change in electricity consumption during the time period. Based on the change in electricity price and the change in electricity consumption during the specified time period, calculate the elasticity coefficient of electricity consumption to electricity price for each type of user during different time periods. Multiple elastic coefficients belonging to the same type of user are arranged in order of time period and combined to form the elastic vector of the corresponding user; By combining the elasticity vectors of all user categories, an elasticity matrix is ​​constructed that reflects the relationship between user electricity consumption behavior and electricity price changes in the entire regional energy system. The process involves combining the latest electricity spot market information and regional energy system operation status data to adjust the parameters of the elasticity matrix. Based on the intraday electricity price data and the adjusted elasticity matrix, the load adjustment amount is determined, including: Based on the supply and demand tension index in the latest electricity spot market information, a first adjustment factor is generated, and based on the first adjustment factor, the elasticity vector of the corresponding time period in the elasticity matrix is ​​corrected for the first time. Based on the deviation between the real-time output and the predicted output of the new energy source in the operating status data, a second adjustment factor is generated. Based on the second adjustment factor, the elastic vector that has been corrected in the first correction is corrected in the second correction to obtain the corrected elastic matrix. Based on the intraday electricity price data and the corrected elasticity matrix, calculate the amount of user load change required to achieve system power balance during the current intraday period; The user load change is allocated according to user category to determine the load adjustment amount for each user category; The step of using model predictive control algorithms to calculate and obtain real-time output adjustment commands for new energy generating units and energy storage systems based on the real-time electricity price data and the load adjustment amount includes: Based on the real-time electricity price data and the load adjustment amount, a real-time optimization problem including system power balance constraints is constructed; By using model predictive control algorithms, the real-time optimization problem is solved in a rolling manner to obtain the planned output adjustment sequence of the new energy generating units and the planned power adjustment sequence of the energy storage system for several future periods. The adjustment amount of the first time period is extracted from the planned output adjustment sequence as the real-time output adjustment instruction for the new energy unit, and the adjustment amount of the first time period is extracted from the planned power adjustment sequence as the real-time power adjustment instruction for the energy storage system. The real-time output adjustment command of the new energy unit and the real-time power adjustment command of the energy storage system are combined to form a real-time output adjustment command.

2. The method for optimizing the combined output of new energy and energy storage based on spot electricity prices across multiple time scales, as described in claim 1, is characterized in that... The process involves determining the thermal storage capacity status based on the first and second temperature change rates, and constructing an optimization objective function that includes the total operating cost of the regional energy system and the renewable energy absorption rate, in conjunction with the day-ahead electricity price data. Based on this optimization objective function, a day-ahead joint dispatch plan is generated, including: The first temperature change rate is compared with a preset first reference rate range, and the second temperature change rate is compared with a preset second reference rate range. Based on the comparison result, the thermal storage capacity status of the energy storage unit is determined, wherein the upper and lower limits of the second reference rate range are numerically smaller than the upper and lower limits of the first reference rate range. Based on the thermal storage capacity status, determine the maximum charging power limit and the maximum discharging power limit that the energy storage unit can provide during the scheduling cycle; Using the maximum charging power limit and the maximum discharging power limit as operating constraints for the energy storage unit, and combining the day-ahead electricity price data, an optimization objective function is constructed that includes the total operating cost of the regional energy system and the renewable energy absorption rate. Solving the optimization objective function yields the planned output value of the new energy unit in each time period within the scheduling cycle, as well as the planned charging power value and planned discharging power value of the energy storage system in each time period within the scheduling cycle. Based on the planned output value, the planned charging power value, and the planned discharging power value, a day-ahead joint scheduling plan is formed.

3. The method for optimizing the combined output of new energy and energy storage based on spot electricity prices across multiple time scales, as described in claim 2, is characterized in that... Solving the objective function yields the planned output of the new energy generating units for each time period within the scheduling cycle, as well as the planned charging power and planned discharging power of the energy storage system for each time period within the scheduling cycle, including: The operating cost item and the new energy consumption item in the optimization objective function are combined into a comprehensive optimization index; Based on the day-ahead electricity price data, the electricity price weighting coefficient for each time period is determined, and the comprehensive optimization index is weighted based on the electricity price weighting coefficient. Using the predicted output range of the new energy unit as the unit output constraint, the optimal solution of the weighted comprehensive optimization index is obtained through iterative calculation under the operating constraint and the unit output constraint. Based on the optimal solution, the planned output values ​​of the new energy generating units in each time period of the scheduling cycle are obtained, as well as the planned charging power value and planned discharging power value of the energy storage system in each time period of the scheduling cycle.

4. A new energy-energy storage joint output optimization system based on spot electricity prices across multiple time scales, characterized in that, include: The acquisition module is used to acquire composite phase change materials of energy storage systems in regional energy systems, time-of-use electricity price data of the electricity spot market, and historical electricity consumption data. The time-of-use electricity price data includes day-ahead electricity price data, intraday electricity price data, and real-time electricity price data. The determination module is used to determine the thermal storage capacity status of the energy storage unit by detecting the temperature change rate of the composite phase change material during the charging and discharging processes. Based on the day-ahead electricity price data and the thermal storage capacity status, in a multi-timescale coordinated optimization framework, with the objectives of optimizing the total operating cost of the regional energy system and maximizing the renewable energy absorption rate, a day-ahead joint scheduling plan for renewable energy units and energy storage systems is generated. The calculation module is used to calculate the elasticity coefficient of electricity price for multiple time periods based on the historical electricity consumption data, and to form an elasticity matrix based on the elasticity coefficient; The correction module is used to combine the latest electricity spot market information and regional energy system operation status data to correct the parameters of the elasticity matrix, and determine the load adjustment amount based on the intraday electricity price data and the corrected elasticity matrix. The adjustment module is used to calculate the real-time output adjustment instructions of the new energy generating units and the energy storage system by using the model predictive control algorithm based on the real-time electricity price data and the load adjustment amount, and to synchronously adjust the output level of the new energy generating units and the charging and discharging power of the energy storage system according to the real-time output adjustment instructions, so as to achieve the joint output optimization of new energy and energy storage. The process involves determining the thermal storage capacity of the energy storage unit by detecting the temperature change rate of the composite phase change material during the charging and discharging processes. Based on the day-ahead electricity price data and the thermal storage capacity status, and within a multi-timescale coordinated optimization framework, a day-ahead joint dispatch plan for the new energy unit and the energy storage system is generated with the objectives of optimizing the total operating cost of the regional energy system and maximizing the renewable energy absorption rate. This plan includes: The surface temperature of the composite phase change material is measured during the charging and discharging processes, and the surface temperature measurement values ​​are recorded within a predetermined time window. Based on the surface temperature measurement, the first temperature change rate of the composite phase change material during the charging process and the second temperature change rate during the releasing process are calculated. Establish a multi-timescale coordination and optimization framework that includes the day-ahead phase; In the day-ahead phase of the multi-timescale coordinated optimization framework, the thermal storage capacity status is determined based on the first temperature change rate and the second temperature change rate. Combined with the day-ahead electricity price data, an optimization objective function is constructed that includes the total operating cost of the regional energy system and the renewable energy absorption rate. Based on the optimization objective function, a day-ahead joint dispatch plan is generated. The elasticity coefficients of electricity prices for different time periods are calculated based on the historical electricity consumption data. An elasticity matrix is ​​then formed based on these elasticity coefficients, including: Extract historical electricity price data and corresponding historical electricity consumption data for each predetermined time period from multiple historical days from the historical electricity consumption data; For two adjacent time periods, based on the historical electricity price data and the corresponding historical electricity consumption data, the electricity price difference between the first time period and the second time period is calculated, and the electricity price difference is used as the change in electricity price during the time period. The user electricity consumption difference between the first time period and the second time period is also calculated, and the user electricity consumption difference is used as the change in electricity consumption during the time period. Based on the change in electricity price and the change in electricity consumption during the specified time period, calculate the elasticity coefficient of electricity consumption to electricity price for each type of user during different time periods. Multiple elastic coefficients belonging to the same type of user are arranged in order of time period and combined to form the elastic vector of the corresponding user; By combining the elasticity vectors of all user categories, an elasticity matrix is ​​constructed that reflects the relationship between user electricity consumption behavior and electricity price changes in the entire regional energy system. The process involves combining the latest electricity spot market information and regional energy system operation status data to adjust the parameters of the elasticity matrix. Based on the intraday electricity price data and the adjusted elasticity matrix, the load adjustment amount is determined, including: Based on the supply and demand tension index in the latest electricity spot market information, a first adjustment factor is generated, and based on the first adjustment factor, the elasticity vector of the corresponding time period in the elasticity matrix is ​​corrected for the first time. Based on the deviation between the real-time output and the predicted output of the new energy source in the operating status data, a second adjustment factor is generated. Based on the second adjustment factor, the elastic vector that has been corrected in the first correction is corrected in the second correction to obtain the corrected elastic matrix. Based on the intraday electricity price data and the corrected elasticity matrix, calculate the amount of user load change required to achieve system power balance during the current intraday period; The user load change is allocated according to user category to determine the load adjustment amount for each user category; The step of using model predictive control algorithms to calculate and obtain real-time output adjustment commands for new energy generating units and energy storage systems based on the real-time electricity price data and the load adjustment amount includes: Based on the real-time electricity price data and the load adjustment amount, a real-time optimization problem including system power balance constraints is constructed; By using model predictive control algorithms, the real-time optimization problem is solved in a rolling manner to obtain the planned output adjustment sequence of the new energy generating units and the planned power adjustment sequence of the energy storage system for several future periods. The adjustment amount of the first time period is extracted from the planned output adjustment sequence as the real-time output adjustment instruction for the new energy unit, and the adjustment amount of the first time period is extracted from the planned power adjustment sequence as the real-time power adjustment instruction for the energy storage system. The real-time output adjustment command of the new energy unit and the real-time power adjustment command of the energy storage system are combined to form a real-time output adjustment command.

5. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the new energy-storage joint output optimization method based on spot electricity prices across multiple time scales as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the implementation of the new energy-storage joint output optimization method based on spot electricity prices across multiple time scales, as described in any one of claims 1 to 3.

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