Green electricity heat supply optimization method fusing load characteristic decomposition and equipment dynamic characteristics

Abstract

The invention belongs to the technical field of optimization of a green electricity heat supply system. The invention provides a green electricity heat supply optimization method fusing load characteristic decomposition and equipment dynamic characteristics, and aims to solve the problems of space-time mismatching, non-quantitative extraction of load characteristics, simplification of the equipment dynamic characteristics and the like caused by renewable energy source intermittency and load volatility in a green electricity heat supply system. Then, through three-layer wavelet decomposition, electric load approximate power and detail power signals are extracted; constructing dynamic models of the electrode boiler and the solid heat storage boiler based on energy balance and dispersing the dynamic models into state space models; further constructing an optimization model with minimum annual total cost and carbon emission as targets; and finally, solving by adopting a multi-target particle swarm algorithm, and realizing energy storage and power grid collaborative energy supply in combination with a power distribution parameter. According to the method, the energy supply stability and the renewable energy consumption rate can be improved, design reference deviation and insufficient equipment output are avoided, economy and environmental protection are both considered, and reliable support is provided for industrial heat energy decarburization.

Description

Technical Field

[0001] This invention relates to the field of green electric heating system optimization technology, specifically to a green electric heating optimization method that integrates load characteristic decomposition and equipment dynamic characteristics. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] As the global energy system accelerates its decarbonization, renewable green electricity, primarily from wind and solar power, is rapidly replacing fossil fuels and becoming a core driver of deep emission reduction in the industrial sector. The planning and implementation of green electricity heating systems are considered a key breakthrough for industrial thermal decarbonization. However, the intermittency of renewable energy and the volatility of loads have led to a spatial and temporal mismatch between output power and industrial electric heating loads, resulting in problems such as unstable power supply and low renewable energy absorption rates in the actual operation of green electricity heating systems.

[0004] Furthermore, existing systems generally lack collaborative analysis of load characteristics and equipment dynamics, failing to quantify and extract high-frequency loads at the second-minute level and low-frequency loads at the hour level, resulting in structural deviations between design benchmarks and extreme operating conditions. At the same time, the dynamic characteristics of equipment such as electrode boilers, thermal storage boilers, and gas boilers vary greatly, making precise and effective control difficult. This leads to the oversimplification of heating slope, minimum start-up and shutdown time, and phase change inertia into a "static efficiency box," causing the optimized capacity to fail to deliver rated output under real dynamic constraints. Summary of the Invention

[0005] To address issues such as the lack of decoupling between high-frequency and low-frequency uncertainties in electrical loads, the simplification of equipment thermal dynamic characteristics, and the fragmentation of optimization and features, this invention provides a green electricity heating optimization method that integrates load feature decomposition and equipment dynamic characteristics. This method constructs a green electricity heating system that coordinates "electricity-heating-storage," integrates load feature decomposition and dynamic operating characteristics, and intelligently optimizes system capacity configuration and dynamic control to achieve high stability, high green electricity ratio, and minimized life-cycle costs in the green electricity heating system.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a green electricity heating optimization method that integrates load characteristic decomposition and equipment dynamic characteristics.

[0007] A green electricity heating optimization method that integrates load characteristic decomposition and equipment dynamic characteristics includes the following processes: Construct a green electric heating system architecture that integrates electricity, heat and storage. The green electric heating system architecture includes a renewable energy power supply end, a heat supply end, and a heat storage end. The heat supply end includes an electrode boiler, and the heat storage end includes a solid thermal storage boiler. The original electrical load signal of the green electric heating system architecture is extracted using three-level wavelet decomposition. By calculating the inner product of the signal and the wavelet basis function and downsampling, the approximate power signal and the detailed power signal are obtained. Dynamic characteristic models for electrode boilers and solid thermal storage boilers are constructed based on energy balance relationships. The dynamic characteristic models are then linearized and discretized into discrete state-space models. Based on approximate power signals, detailed power signals, and discrete state-space models, a dual-objective optimization configuration and scheduling model is constructed to minimize annual total cost and carbon emissions. Annual total cost is obtained by accumulating equipment investment cost, fuel cost, operation and maintenance cost, purchased electricity cost, and unit start-up and shutdown cost. Carbon emissions are calculated by fuel carbon emission intensity and fuel consumption. A multi-objective particle swarm optimization algorithm is used to solve the bi-objective optimization configuration and scheduling model. By updating the particle velocity, position and inertia weight, non-dominated solutions are iteratively screened. Combined with the power allocation parameters, the reference values ​​of battery power and external power grid power are calculated to meet the system energy balance, and the optimal capacity configuration scheme and load scheduling scheme are obtained.

[0008] In one implementation of the first aspect of the present invention, the three-layer wavelet decomposition includes: The wavelet coefficients reflecting the similarity of the signals are obtained by performing an inner product calculation between the original electrical load signal and the preset wavelet basis function. The wavelet coefficients are downsampled to separate the approximate coefficients that characterize the low-frequency trend and the detail coefficients that characterize the high-frequency details. Using the separated approximate coefficients as new signals, the inner product calculation and downsampling operations are repeated, and the decomposition is completed three times in total. The approximate coefficients obtained from the third decomposition are converted into approximate power signals, and the detail coefficients obtained from the three decompositions are converted into detail power signals with different levels of detail. The approximate power signals correspond to low-frequency loads, and the detail power signals correspond to high-frequency loads.

[0009] As a further limitation of the first aspect of the present invention, three power allocation parameters with values ​​ranging from 0 to 1 are set, and the detail power signals of different levels of detail are respectively the first level detail power signal, the second level detail power signal and the third level detail power signal; The battery power reference value is obtained by multiplying the first power allocation parameter by the approximate power signal, the second power allocation parameter by the second level detail power signal, and the third power allocation parameter by the third level detail power signal. The external power grid reference value is obtained by multiplying the first-level detailed power signal by the product of the first power allocation parameter and the approximate power signal, the second power allocation parameter by the product of the second-level detailed power signal, and the third power allocation parameter by the product of the third-level detailed power signal.

[0010] In one implementation of the first aspect of the present invention, the capital recovery coefficient is first calculated based on the interest rate and the service life of the equipment. Then, the capital recovery coefficient is multiplied by the unit capital expenditure of each piece of equipment and the actual installed capacity to obtain the investment cost of a single piece of equipment. The results are then summed to obtain the equipment investment cost. The fuel cost is obtained by multiplying the natural gas price by the natural gas consumption of the gas-fired boiler and the coal price by the coal consumption of the coal-fired combined heat and power system. The fixed maintenance cost is obtained by multiplying the fixed maintenance cost per unit of equipment by the installed capacity, and the variable maintenance cost per unit of equipment is obtained by multiplying the variable maintenance cost by the actual operating power. The two are added together to obtain the operating and maintenance cost. The cost of purchased electricity is obtained by multiplying the grid purchase cost coefficient by the amount of electricity purchased at the corresponding time. The unit start-up and shutdown cost is obtained by multiplying the cost of starting and stopping a single piece of equipment by the number of start-ups and shutdowns.

[0011] In one implementation of the first aspect of the present invention, the process of constructing the discrete state-space model includes: The nonlinear thermodynamic equations of electrode boilers and solid thermal storage boilers are linearized near the steady-state operating point of the equipment to eliminate nonlinear terms. According to the discretization rules, the linearized continuous equations are transformed into discrete equations. Using steam temperature and solid thermal storage boiler energy storage state as state vectors, electrode boiler input power, solid thermal storage boiler input power, and steam flow rate as control vectors, and external disturbances as disturbance vectors, a discrete state-space model is constructed. The discrete state-space model reflects the correlation between the equipment state at the next moment and the current state, control quantity, and disturbance quantity.

[0012] In one implementation of the first aspect of the present invention, the multi-objective particle swarm algorithm updates particle velocity and position, including: Set the maximum and minimum values ​​for the inertia weight. Calculate the current inertia weight according to the current iteration number and the maximum iteration number, following a linear decreasing rule. When calculating the particle's velocity at the next moment, multiply the current velocity by the inertia weight, multiply it by the individual learning factor, a random number between 0 and 1, and the difference between the individual's optimal position and the current position, and then sum this product with the social learning factor, a random number between 0 and 1, and the difference between the global optimal position and the current position. When calculating the particle's position at the next moment, sum the current position and the velocity at the next moment. If the position exceeds the device's capacity range, a correction is made.

[0013] In one implementation of the first aspect of the present invention, carbon emissions include: determining the unit carbon emission intensity of natural gas used in the gas-fired boiler and the unit carbon emission intensity of coal used in the coal-fired cogeneration system; when calculating the carbon emissions of the gas-fired boiler, multiplying the unit carbon emission intensity of natural gas by the natural gas consumption of the gas-fired boiler; when calculating the carbon emissions of the coal-fired cogeneration system, multiplying the unit carbon emission intensity of coal by the coal consumption or power generation of the coal-fired cogeneration system; and summing the two carbon emissions to obtain the total carbon emissions of the system.

[0014] Secondly, the present invention provides a green electric heating optimization system that integrates load characteristic decomposition and equipment dynamic characteristics.

[0015] A green electricity heating optimization system that integrates load characteristic decomposition and equipment dynamic characteristics includes the following processes: The system building unit is configured to: build a green electric heating system architecture that integrates electricity, heat and storage. The green electric heating system architecture includes a renewable energy power supply end, a heat supply end and a heat storage end. The heat supply end includes an electrode boiler and the heat storage end includes a solid thermal storage boiler. The feature extraction unit is configured to: extract features from the original electrical load signal of the green electric heating system architecture using three-level wavelet decomposition; and obtain approximate power signal and detailed power signal by calculating the inner product of the signal and the wavelet basis function and downsampling. The discrete processing unit is configured to: construct dynamic characteristic models for electrode boilers and solid thermal storage boilers based on energy balance relationships, and then linearize and discretize the dynamic characteristic models into discrete state-space models. The scheduling model construction unit is configured to: construct a dual-objective optimization configuration and scheduling model based on approximate power signals, detailed power signals and discrete state space models, with the goal of minimizing annual total cost and carbon emissions. The annual total cost is obtained by accumulating equipment investment costs, fuel costs, operation and maintenance costs, purchased electricity costs and unit start-up and shutdown costs, and the carbon emissions are calculated by fuel carbon emission intensity and fuel consumption. The scheduling scheme generation unit is configured to: use a multi-objective particle swarm optimization algorithm to solve the bi-objective optimization configuration and scheduling model; iteratively filter non-dominated solutions by updating particle velocity, position and inertia weight; and calculate the battery power reference value and the external power grid power reference value in combination with power allocation parameters to meet the system energy balance, thereby obtaining the optimal capacity configuration scheme and load scheduling scheme.

[0016] Thirdly, the present invention provides a computer device, comprising: a processor and a computer-readable storage medium; A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the green electric heating optimization method of the first aspect of the present invention, which integrates load characteristic decomposition and equipment dynamic characteristics.

[0017] Fourthly, the present invention provides a computer-readable storage medium storing a computer program adapted to be loaded by a processor and executed by the green electric heating optimization method of the first aspect of the present invention, which integrates load characteristic decomposition and equipment dynamic characteristics.

[0018] Compared with the prior art, the beneficial effects of the present invention are: This invention employs a comprehensive approach that integrates an electric-heat-storage collaborative architecture, three-layer wavelet decomposition of load characteristics, establishment of a discrete state-space model for equipment, construction of a dual-objective optimization model, and multi-objective particle swarm optimization algorithm. It specifically addresses the spatiotemporal mismatch between output power and electric / heat load caused by the intermittency and load fluctuations of renewable energy, as well as the shortcomings of existing systems lacking collaborative analysis of load characteristics and equipment dynamics. This overcomes operational challenges such as unstable power supply and low renewable energy absorption rates. Simultaneously, it resolves design benchmark deviations caused by the lack of quantified extraction of high and low frequency electrical loads, and the problem of optimized capacity failing to deliver rated output due to the simplification of equipment dynamics to static efficiency boxes. This improves the power supply stability and renewable energy absorption level of green electric heating systems, while ensuring that the optimization scheme balances economic efficiency and environmental friendliness. It avoids system malfunctions or resource waste caused by inaccurate load analysis and distorted equipment models, providing reliable technical support for industrial thermal energy decarbonization.

[0019] This invention employs a scheme that calculates the inner product of the original electrical load signal and wavelet basis functions, performs downsampling to separate coefficients, and repeatedly decomposes the signal three times to transform it into approximate and detailed power signals. This solves the problem that existing systems fail to quantify and extract high-frequency loads at the second-minute level and low-frequency loads at the hour level. It overcomes the structural deviation between design benchmarks and extreme operating conditions caused by inaccurate load characteristic decomposition, and avoids the problem of subsequent capacity configuration and scheduling strategies deviating from actual load characteristics. Through clear decomposition steps, the load is decomposed according to frequency characteristics, improving the accuracy of load characteristic extraction. This allows the approximate power signal to accurately represent low-frequency loads and the detailed power signal to accurately represent high-frequency loads, providing reliable data support for subsequent rational power allocation and dynamic equipment matching. It further ensures that the system formulates optimization schemes according to actual load requirements and avoids power supply imbalances caused by load analysis deviations.

[0020] This invention employs a scheme that sets three power allocation parameters to calculate reference power values ​​for both the battery and the external power grid. This addresses the issues of unclear power allocation rules after load characteristic decomposition and insufficient coordination between energy storage and the grid. It overcomes the power supply fluctuations caused by the intermittency of renewable energy, as well as the shortcomings of existing systems such as low energy storage utilization or excessive grid dependence due to arbitrary power allocation. By rationally allocating loads of different frequencies to energy storage and the grid, it avoids the lifespan loss caused by frequent charging and discharging of energy storage, or the power supply gap caused by instantaneous grid overload. This improves the rationality and flexibility of power allocation, ensuring that the battery can handle stable low-frequency loads while the external grid responds to transient high-frequency loads. Together, they meet the total electricity load demand, smoothing out fluctuations in green electricity output and further enhancing power supply stability and renewable energy absorption rate.

[0021] This invention employs a step-by-step calculation method for equipment investment costs, fuel costs, operation and maintenance costs, purchased electricity costs, and unit start-up and shutdown costs. This solves the problem of incomplete and inaccurate annual total cost calculations in existing systems, which leads to distorted optimization objectives. It overcomes the shortcomings of inaccurate economic assessments of optimization schemes due to missing key items or parameter deviations in cost accounting. It avoids life-cycle economic imbalances caused by incomplete cost considerations (such as focusing only on initial investment while ignoring operation and maintenance costs). By clarifying the calculation logic of each cost, it improves the accuracy and completeness of cost data, providing reliable economic target support for the dual-objective optimization model. This ensures that the optimization scheme, while pursuing carbon emission reduction, also considers life-cycle economics, avoiding insufficient feasibility due to cost calculation deviations, and meeting the needs of coordinated economic and environmental protection in industrial systems.

[0022] This invention linearizes the nonlinear thermodynamic equations of electrode boilers and solid thermal storage boilers, and then discretizes them into state-space models. This addresses the problem in existing systems where the dynamic characteristics of equipment are oversimplified to a "static efficiency box," leading to an inability to achieve rated output with optimized capacity. It overcomes the shortcomings of missing dynamic constraints such as equipment heating slope, minimum start-up and shutdown time, and phase change inertia. This avoids scheduling strategies exceeding the actual operating capacity of the equipment (e.g., requiring the equipment to exceed its dynamic limit output) due to inaccurate equipment models. By accurately describing the relationship between the equipment's next state and its current state, as well as control variables, it improves the accuracy of the equipment's dynamic characteristics, providing realistic dynamic constraints for the optimization model. This ensures that the optimized capacity configuration and scheduling scheme match the actual operating capacity of the equipment, preventing equipment overload or insufficient output, guaranteeing the safe and stable operation of the system, and simultaneously improving the rate of achieving the equipment's rated output.

[0023] This invention employs a scheme that sets a linearly decreasing inertia weight and combines individual and social learning factors to update particle velocity and position. This addresses the problems of low efficiency and susceptibility to local optima in dual-objective optimization models, leading to suboptimal optimization solutions. It overcomes the shortcomings of traditional algorithms, such as insufficient accuracy and slow convergence, and avoids poor solution quality (e.g., only satisfying a single objective) or excessively long convergence cycles caused by the lack of reasonable update rules during iteration. Through reasonable velocity and position update logic, the efficiency and accuracy of the model solution are improved. It can quickly iterate and select non-dominated solutions that balance annual total cost and carbon emissions, avoiding optimization schemes that fail to balance economic and environmental goals due to inaccurate solutions. This ensures that the final capacity configuration and load scheduling scheme is globally optimal, meeting the multi-objective optimization requirements of green electric heating systems.

[0024] This invention employs a step-by-step approach to calculate the carbon emissions of gas-fired boilers and coal-fired combined heat and power (CHP) systems, then sums them to obtain the total carbon emissions. This addresses the problem of unclear and incomplete carbon emission calculations in existing systems, which prevents the accurate achievement of industrial thermal energy decarbonization targets. It overcomes the bias in decarbonization effect assessment caused by missing or distorted carbon emission accounting data, and avoids the failure to meet environmental targets due to inaccurate carbon emission data (such as actual carbon emissions exceeding expected decarbonization requirements). By clarifying the correlation calculation logic between the carbon emission intensity and consumption of different fuels, it improves the accuracy and completeness of carbon emission data, providing reliable environmental target support for the dual-objective optimization model. This ensures that the optimization scheme aligns with the global energy system decarbonization trend, contributes to deep emission reduction in the industrial sector, avoids deviations in the decarbonization path due to carbon emission calculation errors, and effectively promotes the environmental value of green electric heating systems.

[0025] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0026] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0027] Figure 1 A structural diagram of an "electricity-heat-storage" synergistic green electricity heating system is provided as an exemplary embodiment of the present invention; Figure 2 A schematic diagram of wavelet decomposition and reconstruction provided as an exemplary embodiment of the present invention; Figure 3 A dynamic model structural diagram of an electrode boiler and a solid thermal storage boiler in a green electric heating system provided as an exemplary embodiment of the present invention; Figure 4A flowchart illustrating a green electric heating optimization method that integrates load characteristic decomposition and equipment dynamic characteristics, provided as an exemplary embodiment of the present invention. Figure 5 A schematic diagram of a green electric heating optimization system that integrates load characteristic decomposition and equipment dynamic characteristics, provided as an exemplary embodiment of the present invention; Figure 6 A schematic diagram of a computer device provided for an exemplary embodiment of the present invention. Detailed Implementation

[0028] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0029] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0030] This implementation proposes a green electricity heating optimization method that integrates load characteristic decomposition and equipment dynamic characteristics. The green electricity heating system of this implementation is a "electricity-heat-storage" coordinated green electricity heating system, and its architecture is shown in the attached figure. Figure 1 As shown, the renewable energy power supply end consists of wind turbine generators; the thermal end consists of electrode boilers and solid thermal storage boilers; and the thermal storage end consists of high-temperature and low-temperature solid thermal storage boilers. It is divided into three subsystems: Subsystem 1 consists of wind turbine generators with a rated power of 7.5MW; Subsystem 2 includes electrode boilers with rated powers of 12MW, 18MW, and 25MW; and Subsystem 3 consists of a high-temperature solid thermal storage boiler with a rated thermal storage capacity of 9.73MW and a low-temperature solid thermal storage boiler with a rated thermal storage capacity of 6.55MW. The system operates as follows: Subsystem 2 heats ambient water (0.6 MPa, 20℃) and return water (0.6 MPa, 70℃) from Subsystem 3 to saturated water (0.6 MPa, 158℃), which then enters Subsystem 3. In Subsystem 3, some of the saturated water (0.6 MPa, 158℃) is heated to high-pressure steam (8 MPa, 320℃), and its return water is returned to Subsystem 3 for reuse. The remaining saturated water (0.6 MPa, 158℃) is heated to low-pressure steam (0.6 MPa, 158℃), and its return water is returned to Subsystem 2 for heating. The system meets the electrical load demand through wind turbine generators and purchased electricity, meets the high-pressure steam load through a high-temperature solid thermal storage boiler, and meets the low-pressure steam load through a low-temperature solid thermal storage boiler, effectively solving the problem of multi-pressure steam supply.

[0031] In this implementation, the electrical load feature extraction utilizes a three-level wavelet decomposition to decompose the original signal. First, the power signal is decomposed using Haar wavelets. The power signal is then decomposed into high-frequency and low-frequency power signals by passing through a high-pass filter and a low-pass filter, respectively. The low-frequency power signal is then decomposed by the high-pass and low-pass filters sequentially. After three decompositions, approximate power and detailed power signals with decreasing levels of detail are obtained. The final approximate and detailed power signals are then used to allocate power to the energy storage unit and the external power grid according to demand. The load decomposition method is described in [link to relevant documentation]. Figure 2 .

[0032] In this implementation, process simulation software is used to establish dynamic models of the electrode boiler and solid thermal storage boiler. Based on these models, a closed-loop control model of the green electric heating system is established, and the thermal dynamic characteristics of each device during system operation are extracted. First, step disturbances in electrical power or thermal load are applied to the electrode boiler, thermal storage boiler, and gas-fired boiler, respectively, and the dynamic response curves of outlet steam temperature, pressure, and flow rate are simultaneously collected to establish a "capacity-thermal inertia-response speed" mapping database. Then, subspace identification is used to fit the high-order nonlinear thermal process into a discrete state-space dynamic model with fast and slow segmentation. Using thermal storage capacity and equipment power level as measurable disturbance parameters, a multi-parameter model predictive control law is constructed offline, establishing operating constraint equations that reflect the actual operating characteristics of the equipment. The dynamic model construction method is described in [link to documentation]. Figure 3 .

[0033] In this implementation, an optimized configuration and scheduling model for a green electric heating integrated energy system is established, which integrates load characteristic decomposition and equipment dynamic characteristics. The objective function considers the annual total cost and carbon emissions. The cost components include equipment investment cost, operation and maintenance cost, purchased electricity cost, and unit start-up and shutdown cost. System constraints include the operating characteristics of each piece of equipment and system operating constraints. Equipment characteristics include the output characteristics of wind turbine generators, the state-space dynamic model of electric motor boilers, and the state-space dynamic model of solid thermal storage boilers. System operating constraints include equipment operating boundaries, unit ramp-up and start-up and shutdown restrictions, and system energy balance based on wavelet decomposition. Through these constraints, while comprehensively considering flexible equipment scheduling schemes, the system can effectively ensure stable operation and meet load demand, and optimize the configuration and operating performance of the green electric heating system.

[0034] In this implementation, the annual total cost and carbon emissions are used as objective functions. By integrating load characteristic decomposition and equipment dynamic characteristics, an intelligent optimization method for green electric heating systems based on multi-objective particle swarm optimization (PSO) algorithm is established. Based on the optimization iteration information, the inertia weight and the velocity and position of particles are dynamically updated to optimize the capacity configuration of the green electric heating system. Based on the system capacity configuration optimization results, system operation optimization scheduling is performed. By establishing a dynamic energy storage strategy to achieve heat and power decoupling, the system can alleviate the intermittency of wind power while ensuring stable load supply. The optimal power and steam load scheduling scheme of the system is obtained, which effectively promotes the renewable energy consumption level, energy supply stability and flexibility of industrial heating systems.

[0035] This invention constructs a collaborative green electricity heating system model integrating electricity, heat, and energy storage. Based on load characteristic decomposition and equipment dynamics, it achieves system capacity configuration and optimized scheduling, minimizing the system's total lifecycle economic cost. The model meets the following objectives: the electricity and steam generated by the system should meet the system load in real time, ensuring stable operation; dynamic heat storage and release via solid thermal storage boilers achieve dynamic decoupling of heat and electricity, mitigating wind power fluctuations; wind curtailment rate should be minimized to effectively improve the renewable energy absorption rate of the green electricity heating system; and the number of various energy devices in the system should be rationally configured to ensure safe and stable operation while minimizing costs.

[0036] For energy technologies (such as high-pressure electrode boilers and solid thermal storage boilers) of green electricity-driven multi-heat source coupled heating-storage systems, based on the survey of energy technologies, this study uses input-output mapping, parameter correlation, and process simulation methods, along with thermodynamic simulation, to establish mathematical models for each energy technology. The accuracy of the established energy technology models is verified using literature or experimental data, and the models are revised. Finally, a collaborative optimization model for the configuration and scheduling of green electricity heating systems is constructed, using total annual investment and carbon emissions as objective functions.

[0037] The system's design and operational optimization are based on the objective functions of total annual cost (TAC) and carbon emissions (GHGe). TAC can be expressed as: (1); In the formula, For the total annual cost, For capital expenditures, For fuel costs, For operation and maintenance costs, For the cost of purchasing electricity from outside the power grid, The unit start-up and shutdown costs can be represented as follows: (2) (3); (4); (5); (6); (7); (8); In the formula: Indicates each energy device; This indicates their unit capital expenditure; This indicates their actual installed capacity; This indicates the price of natural gas (¥ / kWh). This indicates the total number of days for each typical design period; Typical design days; This represents the number of system operating hours per typical design day; To cover fixed operation and maintenance costs; For variable operation and maintenance costs; , Indicates various energy devices and Relevant cost coefficients; This is the power grid purchase cost coefficient; This refers to the amount of electricity purchased from outside the power grid at the corresponding time. This refers to the start-up and shutdown operation cost of a single unit of the corresponding equipment; This indicates the carbon emission intensity per unit of natural gas used in a gas-fired boiler. This indicates the carbon emission intensity per unit of coal used in a coal-fired combined heat and power (CHP) system.

[0038] In wavelet transform, it is typically necessary to select a suitable wavelet basis function based on the signal characteristics and analysis requirements. The selected wavelet basis function is then convolved with the power signal to obtain wavelet coefficients. These coefficients are then downsampled to obtain approximation coefficients and detail coefficients at different scales. Approximation coefficients represent low-frequency components, while detail coefficients represent high-frequency components. By decomposing the approximation coefficients multiple times, the high-frequency and low-frequency components at different scales can be obtained.

[0039] For a given signal Its inner product calculation can be expressed as: (9); In the formula, The result obtained from the inner product calculation can be considered as the degree of similarity between the signal and the wavelet function under a specific scale and translation. In the formula... The wavelet function is derived from the wavelet basis functions. This is obtained by performing a scaling transformation. For a given scaling parameter *a* and translation parameter *b*, a scaled wavelet function can be obtained. This represents the result of scaling and translating the original signal. The wavelet function after scaling can be expressed by the following formula: (10); In the formula, It is the square root of the scaling factor, used to preserve the energy of the wavelet function.

[0040] By calculating the inner product at different scales and under translation, the scale factor and detail factor of a signal can be extracted. The scale factor reflects the smoothing trend of the signal at a coarser scale, while the detail factor represents the details and high-frequency components of the signal at a finer scale. The scale factor and detail factor can be calculated using the following formula: The scaling factor is: (11); The detail factor is: (12); In the formula, It is a low-pass filter of wavelet function, used to extract the low-frequency components of a signal.

[0041] Wavelet transform can also be used for inverse transform, reconstructing the original signal from the scaling and detail coefficients. The original signal can be recovered by weighted combination of the scaling and detail coefficients. The reconstruction formula is as follows: (13); The above formulas can be used to decompose and reconstruct signals using wavelet decomposition, breaking down the signal into components of different scales and frequencies, and extracting scale coefficients and detail coefficients for further signal analysis and processing.

[0042] Figure 2 This is a schematic diagram of the decomposition and reconstruction of a power signal. Passed through high-pass filters and low-pass filter The signal is decomposed into high-frequency and low-frequency power signals. The low-frequency power signal is then decomposed sequentially by a high-pass filter and a low-pass filter. After three decompositions, an approximate power signal is finally obtained. Detail power from high to low , and .

[0043] Therefore, the approximate power signal and the detailed power signal can be expressed as: (14); (15); In the formula, It is an approximate power signal that can characterize low-frequency loads; It is a detailed power signal that can characterize high-frequency loads.

[0044] The approximate power signal and the detailed power signal are then used to distribute power to the energy storage unit and the external power grid according to demand. For example, low-frequency loads are allocated to the battery that can meet the long-term continuous power supply requirements, while all high-frequency loads are allocated to the external power grid that can meet transient power requirements.

[0045] Dynamic modeling and configuration optimization of electrode boilers and solid thermal storage boilers can be systematically achieved using the state-space method. First, based on the process simulation software Aspen Plus, energy balance equations for the two types of equipment are established, with temperature, stored energy, or pressure as state variables, electrical power and heat charge / discharge power as inputs, and key performance indicators such as steam flow rate and temperature as outputs. This simplifies the equipment model and obtains the relationship between key output indicators and input variables.

[0046] The dynamic model of the electrode boiler is represented as follows: (16); In the formula, The equivalent thermal melting of water, It is the enthalpy of vaporization. The steam flow rate, For electrical power, For the efficiency of electrode boilers, For heat loss, For steam temperature, The ambient temperature.

[0047] The dynamic model of the solid thermal storage boiler is represented as follows: (17); (18); In the formula, This refers to the energy storage state of a solid thermal storage boiler. This refers to the rated capacity of the solid thermal storage boiler. For heat loss, For electrical power, For efficiency, For heat release power, It is a time constant. The exothermic temperature is... The exothermic temperature is a function of the exothermic temperature. The relevant parameters of equation (17-19) can be obtained by using data generated from the dynamic model of Aspen PLUS and through parameter identification methods.

[0048] Secondly, linearizing the above nonlinear thermodynamic equations near the steady-state operating point yields a state-space model that can be used for optimization. After discretization, we get: (19); In the formula, The state vector includes the electrode boiler temperature ( Solid thermal storage boiler thermal storage state ( ) and steam temperature ( )composition, Controlled variables, including electrode boiler input power ( Solid thermal storage boiler input power ( ), steam flow rate ( ), This is a disturbance variable.

[0049] Then, dynamic constraints are embedded into the capacity configuration optimization model, with the goal of minimizing total system cost and carbon emissions. Decision variables include electrode capacity and power, solid thermal storage capacity, and charge / discharge power. The optimization model simultaneously considers operating conditions, power limits, energy storage state constraints, and electrothermal load requirements. Finally, a multi-objective particle swarm optimization method is used to achieve dynamic operation strategy design and long-term capacity coordination, forming a comprehensive thermal energy system configuration scheme that balances rapid response and long-term energy storage.

[0050] This invention employs a multi-objective particle swarm optimization (PSO) algorithm to solve the configuration and scheduling optimization model of a green electricity heating system. The specific steps are as follows: (1) Using energy and load data as input conditions, set the population size, number of iterations and system equipment capacity range, initialize with capacity configuration scheme as particles, decompose the original load signal using three-layer wavelet decomposition, and describe the dynamic characteristics of electrode boiler and solid thermal storage boiler through state space dynamic model. (2) Calculate the power allocation scheme of the system corresponding to each capacity configuration scheme using wavelet decomposition-particle swarm optimization algorithm; calculate the output characteristics of each device using state-space dynamic model; (3) Calculate the fitness value of the green electric heating system, namely the annual total cost and the system CO2 emissions; (4) For all particles, determine their dominance relationship with the fitness values ​​of other particles to determine whether they are non-dominated solutions; (5) Update the inertia weights and the velocity and position of the particles, and correct the out-of-range particles according to the fitness value. The update rules for the velocity, position and inertia weights of the particles are shown in formulas (21), (22) and (23), respectively.

[0051] (twenty one); (twenty two); (twenty three); In the formula, It is a particle In the Speed ​​at the next iteration It is inertial weight. and These are individual and social learning factors, respectively. and A random number between 0 and 1; It is a particle The optimal position of an individual It is a particle Current location It is the globally optimal position; It is a particle In the The position at the next iteration; It is the first Inertia weights in the next iteration and These are the maximum and minimum values ​​of the inertia weight, respectively. It is the current iteration number. It represents the maximum number of iterations.

[0052] (6) Determine if the maximum number of iterations has been reached; otherwise, return to step 2. (7) Repeat steps 2 to 6 until the termination condition is met, and finally obtain the Pareto solution set for the system’s total life cycle cost and CO2 emissions.

[0053] Based on wavelet decomposition theory and the dynamic characteristics of the equipment, power allocation between the lithium battery and the external power grid is required before solving the model. To improve the accuracy of power allocation, the power signal is decomposed into signals of multiple frequencies, and parameters are assigned to different signals. Then, the equipment dynamic characteristic model is fused, and the PSO optimization algorithm is used to determine the optimal power allocation scheme. The basic principles and steps are as follows: (1) Using energy and load data and configuration scheme as input conditions, set the population size, number of iterations and power allocation parameter range. The parameter setting method is as follows: (twenty three); (twenty four); In the formula, For power allocation parameters, and ; This serves as a reference value for battery power, capable of handling most approximate power and a small portion of detailed power with lower precision. It serves as a power reference value and can handle detailed power and a small portion of approximate power.

[0054] (2) Initialize power allocation parameters ; (3) Calculate the power reference values ​​corresponding to the external power grid and the battery based on the parameters, and then obtain key performance indicators such as steam flow and temperature by combining the dynamic characteristics of the electrode boiler and the solid thermal storage boiler. Use these as input data to calculate the particle fitness, that is, the annual total cost and carbon emissions of the green electric heating system. (4) Then, taking the parameters as decision variables and the annual total cost and carbon emission optimization as objectives, the PSO algorithm is used to determine the system power allocation scheme corresponding to different capacity configuration schemes. The proposed multi-objective particle swarm algorithm, which includes wavelet decomposition and equipment characteristics, is as follows: Figure 4 As shown.

[0055] To systematically evaluate the comprehensive advantages of green electricity heating systems, a traditional coal-fired heating system was set as a baseline scenario for comparative analysis. In the baseline scenario, the operating cost is as high as 85.2 million yuan per year, with fuel and carbon emissions accounting for 72% and 20% respectively; annual coal consumption is 1.5 million tons, and carbon emissions are 3.31 million tons. Without energy storage regulation, the annual equipment utilization rate is only 61%, resulting in low energy efficiency. Compared to the baseline scenario, wind power-driven green electricity heating systems demonstrate significant technical and economic advantages. Although the initial investment is 320 million yuan, the zero-fuel nature of wind power reduces annual operating costs by 72.8 million yuan, a 14.6% saving compared to coal; the project's internal rate of return can reach 8% when wind power costs no more than 0.045 yuan / kWh. Cumulative CO2 emission reduction of 31.87 million tons over 20 years. Multi-pressure steam supply through tiered thermal storage improves efficiency by 18.3%.

[0056] The green electricity heating solution replaces coal-fired power with wind power, achieving full green electricity coverage of steam load through the synergy of electrode boilers and staged solid thermal storage. Through a dynamic decoupled operation mode of "storing heat during off-peak hours and releasing heat during peak hours," the system significantly reduces the peak demand of coal-fired standby units, reducing annual coal consumption by 1.5075 million tons and CO2 emissions by 11.5935 million tons. This approach both mitigates wind power fluctuations and significantly reduces fossil fuel dependence and carbon emissions, validating the techno-economic feasibility of green transformation in energy-intensive industries.

[0057] The solution of this invention can achieve renewable energy consumption: reducing the wind curtailment rate to 9.6% and achieving 100% green electricity heating; achieving efficient energy utilization: matching the multi-stage steam supply needs in industrial processes and realizing the cascade utilization of energy; more economical and environmentally friendly: reducing annual operating costs to 72.8 million yuan, saving 14.6% compared to coal-fired power generation; reducing annual coal consumption by 1.5075 million tons and CO2 emissions by 1.5935 million tons; highly scalable: modular design, adaptable to different production capacities, and can be expanded to other industrial processes.

[0058] Figure 5 A green electric heating optimization system integrating load characteristic decomposition and equipment dynamic characteristics is shown, comprising the following processes: System building unit 501 is configured to: build a green electric heating system architecture that integrates electricity, heat and storage. The green electric heating system architecture includes a renewable energy power supply end, a heat supply end and a heat storage end. The heat supply end includes an electrode boiler and the heat storage end includes a solid thermal storage boiler. The feature extraction unit 502 is configured to: extract features from the original electrical load signal of the green electric heating system architecture using three-level wavelet decomposition, and obtain approximate power signal and detailed power signal by calculating the inner product of the signal and the wavelet basis function and downsampling; Discrete processing unit 503 is configured to: construct dynamic characteristic models for electrode boilers and solid thermal storage boilers based on energy balance relationships, and then linearize and discretize the dynamic characteristic models into discrete state space models. The scheduling model construction unit 504 is configured to: construct a dual-objective optimization configuration and scheduling model based on approximate power signals, detailed power signals and discrete state space models, with the goal of minimizing annual total cost and carbon emissions. The annual total cost is obtained by accumulating equipment investment costs, fuel costs, operation and maintenance costs, purchased electricity costs and unit start-up and shutdown costs, and the carbon emissions are calculated by fuel carbon emission intensity and fuel consumption. The scheduling scheme generation unit 505 is configured to: use a multi-objective particle swarm optimization algorithm to solve the bi-objective optimization configuration and scheduling model; iteratively screen non-dominated solutions by updating particle velocity, position and inertia weight; and calculate the battery power reference value and the external power grid power reference value in combination with power allocation parameters to meet the system energy balance, thereby obtaining the optimal capacity configuration scheme and load scheduling scheme.

[0059] It is understood that the aforementioned units can be individually or entirely merged into one or more other units, or some of the units can be further divided into multiple functionally smaller units. This achieves the same operation without affecting the technical effects of the embodiments of the present invention. The aforementioned units are based on logical functional division. In practical applications, the function of one unit can be implemented by multiple units, or the function of multiple units can be implemented by one unit. In other embodiments of the present invention, the system may also include other units. In practical applications, these functions can also be implemented with the assistance of other units, and can be implemented collaboratively by multiple units.

[0060] According to another embodiment of the present invention, the system of this embodiment can be constructed by running a computer program (including program code) capable of performing the steps involved in the corresponding method of the present invention on a general-purpose computing device, such as a computer, which includes processing elements and storage elements such as a central processing unit (CPU), random access memory (RAM), and read-only memory (ROM). The computer program can be recorded on, for example, a computer-readable recording medium, loaded into the aforementioned computing device through the computer-readable recording medium, and run therein.

[0061] Figure 6 A computer device is shown, which includes a processor 601, a communication interface 602, and a computer-readable storage medium 603. The processor 601, communication interface 602, and computer-readable storage medium 603 can be connected via a bus or other means.

[0062] The communication interface 602 is used to receive and send data. The computer-readable storage medium 603 can be stored in the memory of the electronic device. The computer-readable storage medium 603 is used to store computer programs, which include program instructions. The processor 601 is used to execute the program instructions stored in the computer-readable storage medium 603.

[0063] The processor 601 is the computing and control core of an electronic device. It is suitable for implementing one or more instructions, specifically for loading and executing one or more instructions to achieve the corresponding method flow or corresponding function.

[0064] Processor 601 is configured to perform the following procedure: Construct a green electric heating system architecture that integrates electricity, heat and storage. The green electric heating system architecture includes a renewable energy power supply end, a heat supply end, and a heat storage end. The heat supply end includes an electrode boiler, and the heat storage end includes a solid thermal storage boiler. The original electrical load signal of the green electric heating system architecture is extracted using three-level wavelet decomposition. By calculating the inner product of the signal and the wavelet basis function and downsampling, the approximate power signal and the detailed power signal are obtained. Dynamic characteristic models for electrode boilers and solid thermal storage boilers are constructed based on energy balance relationships. The dynamic characteristic models are then linearized and discretized into discrete state-space models. Based on approximate power signals, detailed power signals, and discrete state-space models, a dual-objective optimization configuration and scheduling model is constructed to minimize annual total cost and carbon emissions. Annual total cost is obtained by accumulating equipment investment cost, fuel cost, operation and maintenance cost, purchased electricity cost, and unit start-up and shutdown cost. Carbon emissions are calculated by fuel carbon emission intensity and fuel consumption. A multi-objective particle swarm optimization algorithm is used to solve the bi-objective optimization configuration and scheduling model. By updating the particle velocity, position and inertia weight, non-dominated solutions are iteratively screened. Combined with the power allocation parameters, the reference values ​​of battery power and external power grid power are calculated to meet the system energy balance, and the optimal capacity configuration scheme and load scheduling scheme are obtained.

[0065] This invention also provides a computer-readable storage medium, which is a memory device in an electronic device for storing programs and data. It is understood that the computer-readable storage medium here may include both built-in storage media in the electronic device and extended storage media supported by the electronic device. The computer-readable storage medium provides storage space for storing the processing system of the electronic device.

[0066] Furthermore, this storage space also contains one or more instructions suitable for loading and execution by the processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory; alternatively, it can also be at least one computer-readable storage medium located remotely from the aforementioned processor.

[0067] The present invention also provides a computer program product or computer program comprising computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the following process: Construct a green electric heating system architecture that integrates electricity, heat and storage. The green electric heating system architecture includes a renewable energy power supply end, a heat supply end, and a heat storage end. The heat supply end includes an electrode boiler, and the heat storage end includes a solid thermal storage boiler. The original electrical load signal of the green electric heating system architecture is extracted using three-level wavelet decomposition. By calculating the inner product of the signal and the wavelet basis function and downsampling, the approximate power signal and the detailed power signal are obtained. Dynamic characteristic models for electrode boilers and solid thermal storage boilers are constructed based on energy balance relationships. The dynamic characteristic models are then linearized and discretized into discrete state-space models. Based on approximate power signals, detailed power signals, and discrete state-space models, a dual-objective optimization configuration and scheduling model is constructed to minimize annual total cost and carbon emissions. Annual total cost is obtained by accumulating equipment investment cost, fuel cost, operation and maintenance cost, purchased electricity cost, and unit start-up and shutdown cost. Carbon emissions are calculated by fuel carbon emission intensity and fuel consumption. A multi-objective particle swarm optimization algorithm is used to solve the bi-objective optimization configuration and scheduling model. By updating the particle velocity, position and inertia weight, non-dominated solutions are iteratively screened. Combined with the power allocation parameters, the reference values ​​of battery power and external power grid power are calculated to meet the system energy balance, and the optimal capacity configuration scheme and load scheduling scheme are obtained.

[0068] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. 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 implement the described functions using different methods for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0069] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic cable, digital cable) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data processing device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0070] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A green electricity heating optimization method integrating load characteristic decomposition and equipment dynamic characteristics, characterized in that, Includes the following processes: A green electric heating system architecture that integrates electricity, heat, and storage is constructed. The green electric heating system architecture includes a renewable energy power supply end, a heat supply end, and a heat storage end. The heat supply end includes an electrode boiler, and the heat storage end includes a solid thermal storage boiler. The original electrical load signal of the green electric heating system architecture is extracted using three-level wavelet decomposition. By calculating the inner product of the signal and the wavelet basis function and downsampling, the approximate power signal and the detailed power signal are obtained. Dynamic characteristic models for electrode boilers and solid thermal storage boilers are constructed based on energy balance relationships. The dynamic characteristic models are then linearized and discretized into discrete state-space models. Based on the approximate power signal, detailed power signal, and discrete state space model, a dual-objective optimization configuration and scheduling model is constructed with the goal of minimizing the annual total cost and the carbon emissions. The annual total cost is obtained by summing up the equipment investment cost, fuel cost, operation and maintenance cost, purchased electricity cost, and unit start-up and shutdown cost. The carbon emissions are calculated by the fuel carbon emission intensity and fuel consumption. The bi-objective optimization configuration and scheduling model is solved by using a multi-objective particle swarm optimization algorithm. By updating the particle velocity, position and inertia weight, non-dominated solutions are iteratively selected. The reference values ​​of battery power and external power grid power are calculated in combination with power allocation parameters to meet the system energy balance, and the optimal capacity configuration scheme and load scheduling scheme are obtained.

2. The green electricity heating optimization method based on load characteristic decomposition and equipment dynamic characteristics as described in claim 1, characterized in that, The three-layer wavelet decomposition includes: The wavelet coefficients reflecting the similarity of the signals are obtained by performing an inner product calculation between the original electrical load signal and the preset wavelet basis function. The wavelet coefficients are downsampled to separate the approximate coefficients that characterize the low-frequency trend and the detail coefficients that characterize the high-frequency details. Using the separated approximate coefficients as new signals, the inner product calculation and downsampling operations are repeated, and the decomposition is completed three times in total. The approximate coefficients obtained from the third decomposition are converted into approximate power signals, and the detail coefficients obtained from the three decompositions are converted into detail power signals with different levels of detail. The approximate power signals correspond to low-frequency loads, and the detail power signals correspond to high-frequency loads.

3. The green electricity heating optimization method based on load characteristic decomposition and equipment dynamic characteristics as described in claim 2, characterized in that, Three power allocation parameters are set with values ​​ranging from 0 to 1. The detail power signals of different levels of detail are the first-level detail power signal, the second-level detail power signal, and the third-level detail power signal, respectively. The battery power reference value is obtained by multiplying the first power allocation parameter by the approximate power signal, the second power allocation parameter by the second level detail power signal, and the third power allocation parameter by the third level detail power signal. The external power grid reference value is obtained by multiplying the first-level detailed power signal by the product of the first power allocation parameter and the approximate power signal, the second power allocation parameter by the product of the second-level detailed power signal, and the third power allocation parameter by the product of the third-level detailed power signal.

4. The green electricity heating optimization method based on load characteristic decomposition and equipment dynamic characteristics as described in claim 1, characterized in that, First, calculate the capital recovery factor based on the interest rate and the service life of the equipment. Then, multiply the capital recovery factor by the unit capital expenditure of each piece of equipment and the actual installed capacity to obtain the investment cost of a single piece of equipment. Finally, sum them up to obtain the equipment investment cost. The fuel cost is obtained by multiplying the natural gas price by the natural gas consumption of the gas-fired boiler and the coal price by the coal consumption of the coal-fired combined heat and power system. The fixed maintenance cost is obtained by multiplying the fixed maintenance cost per unit of equipment by the installed capacity, and the variable maintenance cost per unit of equipment is obtained by multiplying the variable maintenance cost by the actual operating power. The two are added together to obtain the operating and maintenance cost. The cost of purchased electricity is obtained by multiplying the grid purchase cost coefficient by the amount of electricity purchased at the corresponding time. The unit start-up and shutdown cost is obtained by multiplying the cost of starting and stopping a single piece of equipment by the number of start-ups and shutdowns.

5. The green electricity heating optimization method based on load characteristic decomposition and equipment dynamic characteristics as described in claim 1, characterized in that, The process of constructing the discrete state-space model includes: The nonlinear thermodynamic equations of electrode boilers and solid thermal storage boilers are linearized near the steady-state operating point of the equipment to eliminate nonlinear terms. According to the discretization rules, the linearized continuous equations are transformed into discrete equations. A discrete state-space model is constructed with steam temperature and solid thermal storage boiler energy storage state as state vectors, electrode boiler input power, solid thermal storage boiler input power, and steam flow rate as control vectors, and external disturbances as disturbance vectors. The discrete state-space model reflects the correlation between the equipment state at the next moment and the current state, control quantity, and disturbance quantity.

6. The green electricity heating optimization method based on load characteristic decomposition and equipment dynamic characteristics as described in claim 1, characterized in that, The multi-objective particle swarm optimization algorithm updates particle velocity and position, including: Set the maximum and minimum values ​​for the inertia weight. Calculate the current inertia weight according to the current iteration number and the maximum iteration number, following a linear decreasing rule. When calculating the particle's velocity at the next moment, multiply the current velocity by the inertia weight, multiply it by the individual learning factor, a random number between 0 and 1, and the difference between the individual's optimal position and the current position, and then sum this product with the social learning factor, a random number between 0 and 1, and the difference between the global optimal position and the current position. When calculating the particle's position at the next moment, sum the current position and the velocity at the next moment. If the position exceeds the device's capacity range, a correction is made.

7. The green electricity heating optimization method based on load characteristic decomposition and equipment dynamic characteristics as described in claim 1, characterized in that, The carbon emissions include: determining the unit carbon emission intensity of natural gas used in gas-fired boilers and the unit carbon emission intensity of coal used in coal-fired cogeneration systems; when calculating the carbon emissions of gas-fired boilers, multiplying the unit carbon emission intensity of natural gas by the natural gas consumption of the gas-fired boilers; when calculating the carbon emissions of coal-fired cogeneration systems, multiplying the unit carbon emission intensity of coal by the coal consumption or power generation of the coal-fired cogeneration system; and summing the two carbon emissions to obtain the total carbon emissions of the system.

8. A green electricity heating optimization system that integrates load characteristic decomposition and equipment dynamic characteristics, characterized in that, Includes the following processes: The system construction unit is configured to: construct a green electric heating system architecture that integrates electricity, heat and storage, wherein the green electric heating system architecture includes a renewable energy power supply end, a heat supply end and a heat storage end, wherein the heat supply end includes an electrode boiler and the heat storage end includes a solid thermal storage boiler; The feature extraction unit is configured to: perform feature extraction on the original electrical load signal of the green electric heating system architecture using three-level wavelet decomposition, and obtain approximate power signal and detailed power signal by calculating the inner product of the signal and the wavelet basis function and downsampling; The discrete processing unit is configured to: construct dynamic characteristic models for electrode boilers and solid thermal storage boilers based on energy balance relationships, and then linearize and discretize the dynamic characteristic models into discrete state-space models. The scheduling model construction unit is configured to: construct a dual-objective optimization configuration and scheduling model based on the approximate power signal, detailed power signal and discrete state space model, with the goal of minimizing the annual total cost and the carbon emissions. The annual total cost is obtained by accumulating the equipment investment cost, fuel cost, operation and maintenance cost, purchased electricity cost and unit start-up and shutdown cost. The carbon emissions are calculated by the fuel carbon emission intensity and fuel consumption. The scheduling scheme generation unit is configured to: use a multi-objective particle swarm optimization algorithm to solve the bi-objective optimization configuration and scheduling model; iteratively filter non-dominated solutions by updating particle velocity, position and inertia weight; and calculate the battery power reference value and the external power grid power reference value in combination with power allocation parameters to meet the system energy balance, thereby obtaining the optimal capacity configuration scheme and load scheduling scheme.

9. A computer device, characterized in that, include: Processor and computer-readable storage media; A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program that, when executed by the processor, implements the green electricity heating optimization method according to any one of claims 1 to 7, which integrates load characteristic decomposition and equipment dynamic characteristics.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program adapted to be loaded by a processor and executed as described in any one of claims 1 to 7 for green electricity heating optimization based on load characteristic decomposition and equipment dynamic characteristics.

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