Multi-element solid energy resource and ccus park virtual power plant electricity-carbon collaborative scheduling method, device and system

Abstract

The application provides a kind of multi-element solid energy resource and the virtual power plant electric-carbon collaborative scheduling method, device and system of CCUS park, method includes: the construction of heterogeneous resource unified aggregation model library for virtual power plant;Based on each type of model in model library, the electric-carbon collaborative multi-time scale optimization scheduling model is constructed with the total running net income maximization of park virtual power plant in scheduling period as objective function, and with carbon emission budget as core constraint;Using distributed robust optimization method, the multiple uncertainty of scheduling model is improved;Based on the alternate direction multiplier method, the improved scheduling model is distributedly solved, the optimal scheduling instruction of each resource is generated, and the instruction is issued through the virtual power plant intelligent control platform.The application can realize the optimal scheduling of the deep cooperation of virtual power plant electric power flow, heat flow and carbon flow, significantly improve the economy, flexibility and robustness of system operation under the premise of guaranteeing the park net zero carbon target.

Description

Technical Field

[0001] This application relates to the field of collaborative scheduling technology, and in particular to a method, apparatus and system for collaborative scheduling of electricity and carbon in a virtual power plant in a CCUS park, involving the energy conversion of multiple solid wastes. Background Technology

[0002] To promote the green and low-carbon transformation of industrial parks, parks integrating diversified solid waste energy conversion with CCUS (Carbon Capture and Storage) technology have become an important direction for exploration. However, their energy system scheduling faces unprecedented complexity, and existing technologies have the following obvious limitations: insufficient consideration of the scheduling of diversified solid waste energy conversion resources, lack of an integrated and collaborative optimization mechanism for "energy flow" and "carbon flow", technical barriers to the aggregation and collaborative scheduling of heterogeneous resources, and poor robustness of models to cope with high-dimensional uncertainty challenges. Summary of the Invention

[0003] The purpose of this application is to provide a method, device, and system for the coordinated scheduling of electricity and carbon in a virtual power plant in a CCUS park, which utilizes a unified aggregation model library of heterogeneous resources and the construction of a multi-timescale optimized scheduling model for electricity and carbon coordination. By improving the uncertainty of the optimized scheduling model and using distributed solution, the optimal scheduling of electricity flow, heat flow, and carbon flow in the virtual power plant can be achieved through deep coordination. This can significantly improve the economy, flexibility, and robustness of the system operation while ensuring the net-zero carbon target of the park.

[0004] Firstly, this application provides a method for coordinated electricity-carbon scheduling of virtual power plants in CCUS parks and multi-source solid waste energy conversion. The method includes: constructing a unified aggregation model library of heterogeneous resources for virtual power plants; the unified aggregation model library includes: a multi-source solid waste energy conversion unit model with a unified virtual power plant scheduling interface, a CCUS deep integration and flexibility model, and a generalized flexible resource aggregation model; based on the various models in the unified aggregation model library, constructing an electricity-carbon coordinated multi-timescale optimization scheduling model with the objective function of maximizing the total net operating revenue of the park's virtual power plants within the scheduling cycle and with carbon emission budget as the core constraint; using the distributed bar optimization method to improve the multiple uncertainties of the multi-timescale optimization scheduling model; and using the alternating direction multiplier method to perform distributed solution of the improved multi-timescale optimization scheduling model to generate the optimal scheduling instructions for each resource, and then issuing the instructions through the virtual power plant intelligent control platform.

[0005] Furthermore, the aforementioned multi-element solid waste energy generation unit model includes: a power generation / heating efficiency model based on fuel calorific value and composition, a thermoelectric coupling characteristic model, and a dynamic optimization allocation model for multiple solid waste fuels among multiple units; the CCUS deep integration and flexibility model includes: a dynamic relationship model between carbon capture rate and energy consumption, and an adjustable load characteristic model; the generalized flexible resource aggregation model is a unified abstraction of electrochemical energy storage, thermal storage tanks, electric vehicle charging and swapping networks, and interruptible industrial processes into a flexible resource model with specific capacity, power, response time, cycle cost, and spatiotemporal transfer constraints.

[0006] Furthermore, in the aforementioned electricity-carbon coordinated multi-timescale scheduling model, the total net operating revenue is calculated based on electricity / heat sales revenue, ancillary service market revenue, carbon trading revenue, fuel costs, operation and maintenance costs, purchased electricity costs, and the operating energy consumption costs of CCUS. The core constraints introduce carbon flow balance and budget constraints as system constraints of equal importance to electricity balance and heat balance. Carbon flow balance and budget constraints include: real-time carbon flow balance equations and the requirement that the cumulative net carbon emissions within the scheduling cycle do not exceed the set carbon budget. The multi-timescale scheduling method includes: using virtual power plants as the executing entity, and executing on a rolling basis across three timescales. The three time scales include: the day-ahead optimization layer, the intraday rolling optimization layer, and the real-time control layer. The day-ahead optimization layer is used to optimize and determine the start-up and shutdown plans of each unit, the basic output curve, the CCUS planned capture rate curve, the energy storage baseline plan, and the flexible load baseline for the next day based on forecasts. The intraday rolling optimization layer is used to continuously optimize and adjust the output of controllable resources based on the plans determined by the day-ahead optimization layer, according to the latest ultra-short-term forecast data and actual operating deviations. The real-time control layer is used to decompose and distribute the optimal dispatch instructions to the local control systems of each resource through the virtual power plant intelligent control platform, and implement second-level frequency and power support.

[0007] Furthermore, the above-mentioned sub-Bruker bar optimization method is used to improve the multiple uncertainties of the multi-time-scale optimization scheduling model, including: the sub-Bruker bar optimization method based on Wasserstein distance to handle the uncertain parameters involved in the multi-time-scale optimization scheduling model; the uncertain parameters include: solid waste supply, photovoltaic / wind power output, and market electricity price.

[0008] Furthermore, the above-mentioned distributed solution of the improved multi-timescale optimization scheduling model based on the alternating direction multiplier method generates the optimal scheduling instructions for each resource. This includes: decomposing the global optimization problem into multiple sub-problems based on the physical boundaries or operation and management rights of the resources within the park; each sub-problem contains the model, constraints, and private data of all resources within the subject or subsystem; using the alternating direction multiplier method, information exchange content and iterative processes are executed based on multiple sub-problems until the convergence condition is met, at which point the iteration stops, and the optimal scheduling instructions for each resource are obtained.

[0009] Furthermore, in the above-mentioned information exchange and iterative process based on multiple sub-problems, each sub-problem iteratively exchanges the following information with the virtual power plant center: The information reported by each sub-problem to the virtual power plant center includes: the resource scheduling plan under the current iteration step; the resource scheduling plan includes: unit output, carbon capture rate, energy storage charging and discharging power, and load adjustment amount; The information issued by the virtual power plant center to each sub-problem includes: system-level coordination signals; the coordination signals include: marginal carbon emission cost signal, system power balance signal, and Lagrange multiplier update amount.

[0010] Furthermore, the above convergence conditions include one of the following: the original residual and the dual residual are less than a set threshold; the change in the global objective function value in two adjacent iterations is less than the convergence accuracy; or the preset maximum number of iterations is reached.

[0011] Secondly, this application also provides a device for coordinated electricity-carbon scheduling of virtual power plants in CCUS parks and multi-source solid waste energy conversion. The device includes: a model library construction module for constructing a unified aggregation model library of heterogeneous resources for virtual power plants; the unified aggregation model library of heterogeneous resources includes: a multi-source solid waste energy conversion unit model with a unified virtual power plant scheduling interface, a CCUS deep integration and flexibility model, and a generalized flexible resource aggregation model; an optimization scheduling model construction module for constructing a multi-timescale optimized scheduling model of electricity-carbon coordination based on various models in the unified aggregation model library of heterogeneous resources, with the objective function of maximizing the total net operating revenue of the park's virtual power plants within the scheduling cycle and the carbon emission budget as the core constraint; an uncertainty improvement module for improving the multiple uncertainties of the multi-timescale optimized scheduling model using the sub-Bruker optimization method; and an optimal instruction generation module for performing distributed solution on the improved multi-timescale optimized scheduling model based on the alternating direction multiplier method, generating optimal scheduling instructions for each resource, and issuing the instructions through the virtual power plant intelligent control platform.

[0012] Thirdly, this application also provides a virtual power plant electricity-carbon coordinated dispatching system for multi-element solid waste energy conversion and CCUS parks. The system includes: a server, an intelligent control platform, and an execution system connected in sequence; the server is used to execute the method described in the first aspect and issue optimal dispatching instructions to the intelligent control platform; the intelligent control platform performs dispatching control on the execution system according to the optimal dispatching instructions; the execution system includes: multi-element solid waste unit, CCUS coupled unit, renewable energy management system, energy storage system, and flexible load.

[0013] Fourthly, this application also provides a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the method described in the first aspect above.

[0014] The method, apparatus, and system for energy conversion from diverse solid wastes and virtual power plant-carbon coordinated scheduling in CCUS industrial parks provided in this application represent a smart scheduling approach for virtual power plants that can uniformly aggregate and optimize resources with diverse characteristics within the park, achieving deep coordination of power flow, heat flow, and carbon flow. This aims to solve the comprehensive optimization and control challenges in the transition from high-carbon to zero-carbon industrial parks. Specifically, through the construction of a unified aggregation model library for heterogeneous resources and a multi-timescale optimization scheduling model for electricity-carbon coordination, as well as improvements to the uncertainty of the optimization scheduling model and distributed solution, optimal scheduling of deep coordination of power flow, heat flow, and carbon flow in the virtual power plant is achieved. This significantly improves the system's economy, flexibility, and robustness while ensuring the park's net-zero carbon target. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific 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 from these drawings without creative effort.

[0016] Figure 1 A flowchart of a method for co-scheduling electricity and carbon emissions from a virtual power plant in a CCUS park, provided as an embodiment of this application; Figure 2 A schematic diagram of a multi-element solid waste energy conversion and CCUS park virtual power plant system architecture provided in this application embodiment; Figure 3 A schematic diagram of another multi-element solid waste energy conversion and CCUS park virtual power plant system architecture provided in this application embodiment; Figure 4 A structural block diagram of a multi-solid waste energy conversion and CCUS park virtual power plant electricity-carbon co-dispatch device provided in this application embodiment; Figure 5 This is a schematic diagram of a multi-element solid waste energy conversion and CCUS park virtual power plant electricity-carbon coordinated dispatch system provided in the embodiments of this application. Detailed Implementation

[0017] The technical solutions of this application will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] Current technology has the following shortcomings: (1) Insufficient consideration of scheduling of diverse solid waste energy resources: Existing studies usually simplify solid waste incineration power generation units as stable power sources, ignoring the huge differences and randomness of different fuels in terms of supply, calorific value, and seasonality. They fail to model the optimal allocation of fuels among multiple units, the coupling characteristics of cogeneration, and the constraints of co-processing of pollutants, resulting in the disconnect between scheduling plans and actual operating conditions.

[0019] (2) Lack of an integrated and coordinated optimization mechanism for "energy flow" and "carbon flow": Traditional dispatching prioritizes economic efficiency, with carbon emissions only used as a post-event statistical indicator. For parks equipped with CCUS, most existing methods treat them as "end-of-pipe treatment" facilities with fixed operating parameters, failing to incorporate their adjustable carbon capture rate and its strongly correlated energy consumption as core decision variables into the dispatching model. This makes it impossible to achieve a dynamic global optimal trade-off between electricity market revenue, carbon market revenue, and CCUS operating energy consumption.

[0020] (3) There are technical barriers to the aggregation and coordinated scheduling of heterogeneous resources: Resources within the park are highly heterogeneous in terms of physical characteristics, response time, control objectives and ownership. Existing scheduling models lack a universal framework that can uniformly describe and efficiently coordinate such heterogeneous resources, and the potential for system-level flexibility and complementary benefits have not been fully explored.

[0021] (4) Poor robustness of models for dealing with high-dimensional uncertainty challenges: The park is simultaneously subjected to multiple risks such as fluctuations in renewable energy output, uncertainties in the supply of various solid waste fuels, and fluctuations in market electricity prices. Traditional deterministic optimization or single uncertainty handling methods are difficult to achieve a good balance between computational complexity and the robustness of scheduling schemes.

[0022] Based on this, this application provides a method, apparatus, and system for the coordinated scheduling of electricity and carbon in a virtual power plant within a CCUS (Concentrated Environmental Protection and Research Center) park, utilizing a unified aggregation model library of heterogeneous resources and constructing a multi-timescale optimized scheduling model for electricity and carbon synergy. By improving the uncertainty of the optimized scheduling model and employing distributed solution, it achieves optimal scheduling of the power flow, heat flow, and carbon flow of the virtual power plant, significantly enhancing the system's economy, flexibility, and robustness while ensuring the park's net-zero carbon target. To facilitate understanding of this embodiment, a detailed description of the electricity-carbon coordinated scheduling method for a virtual power plant within a CCUS park, disclosed in this application, will be provided first.

[0023] This application provides a method for coordinated scheduling of electricity and carbon in a virtual power plant in a CCUS park, based on the energy conversion of multi-solid waste. The core of this method is to construct a three-layer optimization framework of “refined resource modeling, multi-objective coordination of electricity and carbon, and robust decision-making under uncertainty”, with the virtual power plant as the central hub for coordinated scheduling. Figure 1 Here is a flowchart of the method, which specifically includes the following steps: Step S102: Construct a unified aggregation model library for heterogeneous resources for virtual power plants; the unified aggregation model library for heterogeneous resources includes: a multi-element solid waste energy unit model with a unified scheduling interface for virtual power plants, a CCUS deep integration and flexibility model, and a generalized flexible resource aggregation model. In practical implementation, differentiated mathematical models are established for the unique resource types of the industrial park, and a unified aggregation model library of heterogeneous resources is constructed to provide a unified scheduling interface for virtual power plants. The differentiated mathematical models include three types: a multi-element solid waste energy conversion unit model, a CCUS deep integration and flexibility model, and a generalized flexible resource aggregation model.

[0024] Among them, the multi-solid waste energy unit model: in order to distinguish between unit types such as municipal solid waste incineration, industrial solid waste incineration, biomass direct combustion, and coal-fired coupled with sludge, it specifically includes: a power generation / heating efficiency model based on fuel calorific value and composition, a thermoelectric coupling characteristic model, and a dynamic optimization allocation model of multiple solid waste fuels among multiple units.

[0025] CCUS Deep Integration and Flexible Model: The CCUS device and its host generator unit are bound together and modeled as a whole. Key models include: dynamic relationship model between carbon capture rate and energy consumption, and adjustable load characteristic model.

[0026] The generalized flexible resource aggregation model is a unified abstraction of electrochemical energy storage, thermal storage tanks, electric vehicle charging and swapping networks, and interruptible industrial processes into a flexible resource model with specific capacity, power, response time, cycle cost, and spatiotemporal transfer constraints.

[0027] Step S104: Based on various models in the heterogeneous resource unified aggregation model library, construct an electricity-carbon collaborative multi-timescale optimization scheduling model with the objective function of maximizing the total net operating revenue of the virtual power plant in the park within the scheduling cycle and the carbon emission budget as the core constraint. This model is the core of the method in this embodiment. It coordinates economic and carbon objectives within a unified mathematical framework and uses a virtual power plant as the optimization subject.

[0028] Objective function: The objective is to maximize the total net operating revenue of the virtual power plant in the park within the scheduling period. Net revenue calculation comprehensively considers: electricity / heat sales revenue, ancillary service market revenue, carbon trading revenue, fuel costs, operation and maintenance costs, purchased electricity costs, and the operating energy consumption costs of CCUS. These revenues or costs can be determined using the various models in the aforementioned model library.

[0029] Core constraints: Carbon flow balance and budget constraints are introduced as system constraints of equal importance to power balance and heat balance. Specifically, these include: ① real-time carbon flow balance equations; ② the cumulative net carbon emissions within the scheduling cycle do not exceed the set carbon budget. These constraints can also be determined based on carbon emission-related models in the model library.

[0030] Multi-timescale implementation: This model uses a virtual power plant as the execution entity and executes it on a rolling basis across three timescales: 1) Day-ahead optimization layer: Based on forecasts, optimize and determine the start-up and shutdown plans of each unit, the basic output curve, the CCUS planned capture rate curve, the energy storage baseline plan, and the flexible load baseline for the next day.

[0031] 2) Intraday Rolling Optimization Layer (15 minutes) (1-hour level): Based on the daily plan, and according to the latest ultra-short-term forecast data and actual operational deviations, the output of controllable resources is continuously optimized and adjusted, especially the real-time capture rate of CCUS is finely adjusted.

[0032] 3) Real-time control layer (second-level) (Minute-level): Through the virtual power plant intelligent control platform, optimization instructions are decomposed and distributed to the local control systems of each resource, implementing frequency and power support at the second level.

[0033] It should be noted that the aforementioned unified aggregation model library for heterogeneous resources is actually obtained by uniformly aggregating and modeling various resources (distributed photovoltaic, energy storage, controllable loads, gas turbines, grid power purchase and sale, etc.) within the virtual power plant. Therefore, based on the models in the library, the output, cost, and carbon emission characteristics of each resource can be clearly defined. Furthermore, the total net operating revenue of the virtual power plant can be calculated based on electricity / heat sales revenue, ancillary service market revenue, carbon trading revenue, fuel costs, operation and maintenance costs, purchased electricity costs, and CCUS operating energy consumption costs, thereby determining the objective function that maximizes the total net operating revenue of the virtual power plant within the scheduling cycle. On the other hand, carbon emission budget constraints can also be determined based on the resource models, thereby constructing an electricity-carbon collaborative multi-timescale optimization scheduling model with the carbon emission budget as the core constraint.

[0034] Step S106: The multi-time-scale optimization scheduling model is improved by using the sub-Bruker optimization method. The original virtual power plant scheduling model in the park was based on the assumption that "photovoltaic output, load demand, electricity price, carbon price, and carbon emission coefficient of gas turbine units are all fixed values" to calculate the optimal solution. However, in actual operation, all these data are uncertain. For example, photovoltaic output may be lower than the predicted value due to cloudy days, and carbon price may suddenly rise. These "uncertainties" will cause the originally calculated optimal solution to either have a significant decrease in revenue or exceed the carbon emission budget.

[0035] To enhance the robustness of the virtual power plant scheduling scheme in actual operation, this embodiment employs a biblical optimization method based on Wasserstein distance to handle key uncertainties such as solid waste supply, photovoltaic / wind power output, and market electricity prices. This method constructs a "fuzzy set" centered on an empirical distribution using historical data, and seeks the scheduling scheme that minimizes the expected operating cost of the virtual power plant under the "worst-case" condition among all possible distributions contained in this fuzzy set. The specific implementation process will be detailed later.

[0036] Step S108: The improved multi-timescale optimization scheduling model is solved in a distributed manner based on the alternating direction multiplier method to generate the optimal scheduling instructions for each resource, and the instructions are issued through the virtual power plant intelligent control platform.

[0037] To protect the data privacy of different operating entities within the park and improve the efficiency of solving large-scale optimization problems, this embodiment designs a distributed solution mechanism based on a virtual power plant architecture. Specifically, it includes the following implementation steps: Sub-problem decomposition: Based on the physical boundaries or operational management rights of resources within the park, the global optimization problem is decomposed into multiple sub-problems. Typical decomposition methods include: (1) Divided by operating entity, such as solid waste disposal companies, power generation companies, energy storage operators, and charging service providers, each is a sub-problem; (2) Divided by physical subsystems, such as “solid waste energy unit + CCUS”, “photovoltaic + energy storage”, “flexible load cluster”, etc., each is a sub-problem.

[0038] Each of the above sub-problems contains the model, constraints, and private data of all resources within that subject or subsystem.

[0039] Information exchange content and iterative process: The alternating direction multiplier method is used for collaborative solution. Each subproblem iteratively exchanges the following information with the virtual power plant center: 1. The information reported by each sub-problem to the virtual power plant center includes: the resource scheduling plan under the current iteration step (such as unit output, carbon capture rate, energy storage charging and discharging power, load adjustment, etc.).

[0040] 2. The information sent by the virtual power plant center to each sub-problem includes: system-level coordination signals, mainly including: Marginal carbon emission cost signal (λ carbon): reflects the tightness of the system's overall carbon budget; System power balance signals (λelectricity, λheat): reflect the balance between power supply and demand; The Lagrange multiplier update is used to guide each subproblem to adjust its own plan to meet global constraints.

[0041] Each subproblem updates the Lagrange term in its objective function locally based on the received coordination signal, re-solves to optimize its own resources, and then reports the updated plan. This process is repeated iteratively.

[0042] The above convergence condition is set as follows: the iteration process continues until one of the following convergence conditions is met: (1) The original residual and the dual residual are less than the set threshold (such as ε1, ε2), that is, the matching error between the scheduling plan of each sub-problem and the global constraints of the system is small enough; (2) The change in the global objective function value between two adjacent iterations is less than the convergence accuracy (e.g., ΔJ < ε3); (3) Reach the preset maximum number of iterations (safe convergence guarantee).

[0043] When the convergence condition is met, the virtual power plant center outputs the optimal scheduling instructions for each resource, ultimately causing the solutions to all subproblems to converge to the global optimal solution.

[0044] The aforementioned intelligent control platform, serving as the core control hub of the virtual power plant, communicates with the local control systems of various resources within the park. It receives the optimal scheduling instructions generated by the distributed solution module, decomposes and distributes them to each resource for execution, and simultaneously collects actual operating data to feed back to the upstream module.

[0045] The proposed method for co-scheduling electricity and carbon emissions from the virtual power plant in the CCUS (Content Control and Renewable Energy System) park, based on the embodiment of this application, aims to maximize the value of solid waste energy conversion within the park, optimize the energy efficiency of the CCUS system, achieve full renewable energy consumption, and precisely control net carbon emissions by constructing a refined heterogeneous resource model and co-optimization framework centered on the virtual power plant. This provides core intelligent decision support for the zero-carbon transformation of industrial parks.

[0046] The specific implementation process of step S106 above, which uses the sub-Bruker optimization method to improve the multiple uncertainties of the multi-time-scale optimization scheduling model, is as follows: 1) Identify and unify all uncertainty parameters First, identify all the key variables in the scheduling model that are prone to fluctuation and unpredictability: The actual supply of solid waste fuel (which affects the generating capacity of the unit), the actual output of photovoltaic and wind power (renewable energy fluctuations), and the market purchase and sale price of electricity (which affects economic efficiency) are all parameters that cannot be accurately predicted. These are the main reasons why dispatching schemes deviate from the optimal or even become infeasible.

[0047] 2. Construct an empirical distribution using historical data. Instead of arbitrarily assuming that these uncertainties follow a normal or beta distribution, we directly construct an empirical distribution using historical measured data. This can be understood as: the most reliable "basic distribution" is the situation that actually occurred in history.

[0048] 3. Construct Wasserstein fuzzy sets centered on the empirical distribution. This is the most crucial step in Wasserstein DRO: Using the previously obtained empirical distribution as the center, set a radius (usually called the fuzziness coefficient / conservatism) to encompass all probability distributions that are "not too far away" from the empirical distribution, forming a fuzzy distribution set.

[0049] The core message: The "most likely scenario" as told by historical data; Radius: The degree to which the actual distribution is allowed to deviate from its historical value; Fuzzy set: The set of all reasonable, possible, and realistic probability distributions.

[0050] The advantage of doing this is that it does not force a unique distribution, but only acknowledges that "the true distribution is within this reasonable range".

[0051] 4. Find the "worst distribution" in the fuzzy set. The model does not calculate based on the most ideal situation, but rather finds the "worst-case distribution" among all possible distributions contained in the fuzzy set that would result in the highest operating cost and worst economic efficiency for the virtual power plant.

[0052] This worst-case distribution represents the most unfavorable but still reasonable combination of scenarios: insufficient solid waste supply, low wind and solar power output, and high market electricity prices.

[0053] 5. Minimize the expected running cost under the worst-case distribution. After determining the "worst-case distribution", the optimization objective becomes: Under this worst-case distribution, the goal is to minimize the expected operating cost of the virtual power plant.

[0054] In other words, even if the most unfavorable situation is encountered in the future, this scheduling strategy will still be the most stable, the most economical, and the one with the least loss.

[0055] 6. Transform robust optimization into a solvable conventional optimization model. By utilizing the mathematical properties of the Wasserstein distance, this complex optimization problem of "minimizing cost under the worst distribution" can be transformed into a conventional convex optimization / linear programming / second-order cone programming problem, which can be directly computed using solvers such as Gurobi and CPLEX.

[0056] 7. Embedded Electric-Carbon Synergistic Multi-Timescale Scheduling Framework Finally, this Wasserstein degenerate bar optimization logic was embedded into the original electricity-carbon collaborative multi-timescale scheduling model: Day-ahead scheduling: making robust global decisions based on long-term uncertainties; Intraday rolling: Update the fuzzy set based on real-time information and revise the scheduling plan; The entire process meets the following constraints: power balance, unit operation, energy storage, and carbon emission budget.

[0057] See Figure 2 The diagram shown illustrates the system architecture of the diversified solid waste energy conversion and CCUS campus virtual power plant. The overall architecture includes the following modules: 1. Model building module Inputs: Historical and real-time data, park resource model library; Output: Unified aggregation model for heterogeneous resources; Purpose: To provide a standardized model interface for optimizing scheduling.

[0058] The model building module first combines the park's historical operational data, current real-time data, and the existing park resource model library. It then unifies and aligns information on different types and formats of resources, such as electricity, water, gas, heat, equipment, energy storage, and photovoltaics. Following the standard structure in the resource model library, it models and aggregates these scattered and heterogeneous resources one by one, ultimately forming a unified aggregated model that comprehensively describes the park's resource status, capabilities, and constraints. This model is output in a fixed and universal format, becoming a standardized model interface. This allows the subsequent optimization and scheduling module to directly call upon the unified data and model to perform scheduling calculations without needing to consider the differences in underlying equipment.

[0059] 2. Optimize the scheduling module Inputs: resource model, market policy parameters, feedback data; Output: Electricity-carbon collaborative optimization scheduling model; Function: To establish a rolling optimization model with multiple time scales.

[0060] The optimized scheduling module first uses the established unified resource model as a foundation, combined with external parameters such as market electricity prices, carbon emission policies, and reward and punishment mechanisms. It also receives real-time feedback data after the system starts operating, comprehensively analyzes the dual objectives of power balance and carbon emission control, and through optimized calculations, forms a power scheduling system that takes into account both electricity costs and carbon emissions. A carbon-coordinated optimization scheduling scheme is developed and encapsulated into a unified scheduling model. Based on this, the model is continuously updated and rolled over according to different time periods such as seconds, minutes, hours, and days. Ultimately, a multi-time-scale rolling optimization model is established that can adapt to the real-time operation and long-term planning of the park, providing continuous and accurate decision-making basis for park resource scheduling.

[0061] 3. Uncertainty handling module Input: Optimized model + historical data; Output: Robust scheduling model; Function: Optimization of Bruker bars based on Wasserstein distance.

[0062] The uncertainty handling module, based on the existing optimized scheduling model and combined with fluctuation patterns and uncertainty information extracted from historical operating data, identifies the potential fluctuation range and characteristics of photovoltaic output, load, and electricity price through data analysis. Building on this, the module uses Wasserstein distance to characterize the fuzzy probability distribution of uncertain factors. While ensuring the model can withstand various extreme but reasonable disturbances, it strengthens and corrects the original optimized scheduling model, ultimately outputting a robust scheduling model. This completes the Wasserstein distance-based robust optimization, ensuring the scheduling scheme remains safe, stable, and feasible in the face of various uncertainties.

[0063] 4. Distributed Solver Module Input: Robust optimization model; Output: Optimal scheduling instruction; Purpose: ADMM distributed solution, privacy protection, and parallel computing.

[0064] The distributed solution module is based on a pre-built robust optimization model and employs the ADMM (Alternating Directional Multiplier Method) for computation. It breaks down the originally centralized large-scale optimization problem into multiple interconnected but relatively independent subproblems, distributing them to different nodes for parallel computation. This improves the solution speed while ensuring that nodes only exchange necessary intermediate results, avoiding direct transmission of raw data and effectively protecting data privacy. After completing the distributed iterative computation, the module aggregates and integrates the results, ultimately outputting a safe and feasible optimal scheduling instruction.

[0065] 5. Intelligent control platform Input: Scheduling instructions; Output: Control commands are issued; Functions: instruction decomposition, real-time control, and data acquisition.

[0066] The instruction flow in the above system architecture includes the following two flows: (1) Forward instruction flow: Model building → Optimized scheduling → Uncertainty handling → Distributed solution → Intelligent control → Resource execution; (2) Feedback data flow: resource execution data → intelligent control platform → optimization scheduling module (forming a closed loop).

[0067] The distributed coordination process includes: Iterative exchange between the virtual power plant center and its sub-problems: The center issued signals regarding marginal carbon cost and power balance. Sub-problem reporting: Local resource scheduling plan; Continue until convergence, then output the globally optimal scheduling instruction.

[0068] See Figure 3 The diagram shows another system architecture for diversified solid waste energy conversion and CCUS park virtual power plant, illustrating the contents or related modules at each level.

[0069] The following is a specific example: taking a comprehensive zero-carbon demonstration park as an example: 1. System Configuration and Basic Data This embodiment simulates a typical industrial park encompassing multi-purpose solid waste disposal and CCUS, whose key energy facilities include: Solid waste energy conversion segment: 1 300-ton / day municipal solid waste incinerator (equipped with a 12MW extraction condensing unit); 1 750-ton / day industrial solid waste incineration waste heat boiler (equipped with a 10MW back pressure unit); 1 130-ton / hour coal-fired circulating fluidized bed boiler (coupled with municipal sludge treatment, equipped with a 15MW back pressure unit).

[0070] CCUS Unit: This unit is integrated with the aforementioned coal-fired power plant and employs amine-based post-combustion capture technology. Its designed capture capacity is 100 tons of CO2 per hour, and the capture rate can be continuously adjusted within the range of 50% to 90%. The empirical formula for its unit capture energy consumption model is: Energy Consumption (MWh) = A + B × (Capture Rate - 0.5), where A and B are coefficients determined based on the specific equipment.

[0071] Other resources: A 20MW distributed photovoltaic power station will be built on the roof of the park; a 10MW / 20MWh lithium battery energy storage system will be configured; and a smart charging pile network with a total power of about 5MW will be available, of which 50% of the power has the potential for dispatch.

[0072] Market and Policy Environment: The park participates in the provincial electricity market and adopts peak-valley time-of-use pricing. The national carbon market allowance price is 100 yuan / ton CO2. The park sets its own daily carbon budget of no more than 200 tons of net carbon emissions per day.

[0073] 2. Experimental Design and Comparison Scheme To verify the effectiveness of the method and system of this invention, the following four scheduling scenarios were designed for comparative analysis: Scenario A (Traditional Economic Dispatch): Adopting the principle of "heat-based power supply", CCUS operates at a fixed high capture rate (85%), only optimizing the cost of electricity purchase, as the benchmark scenario.

[0074] Scenario B (This embodiment - no carbon constraints): The scheduling framework of this embodiment is applied, but the carbon budget constraint is temporarily removed, and only the economic efficiency is optimized.

[0075] Scenario C (This embodiment - Electricity-Carbon Synergy): The method and system of this embodiment are fully applied, that is, the economic goals and the hard constraint of daily net carbon emissions not exceeding 200 tons are considered at the same time.

[0076] Scenario D (Deterministic Ideal Optimization): Assuming that all uncertain parameters can be accurately predicted, optimization is performed under this ideal condition as a theoretical optimal reference.

[0077] 3. Scheduling Process and Key Results Using the method and system of this embodiment, the operation of a typical day is simulated and scheduled. The optimization model constructs a Wasserstein fuzzy set of uncertainty parameters based on historical data and solves it using a distributed algorithm. The main simulation results are compared in the table below: Table 1

[0078] 4. Results Analysis and Conclusions Economic validation of electricity-carbon synergy: Scenario C achieved a 7.2% cost reduction while meeting a strict carbon budget (198 tons < 200 tons), outperforming Scenario B (4.5%) which only considered economics. This reveals the value of electricity-carbon synergy scheduling: by dynamically reducing the CCUS capture rate during peak electricity price periods to save energy costs, and increasing the capture rate during off-peak electricity price periods, a better economic balance can be found while meeting total carbon constraints.

[0079] Resource optimization effect: The "solid waste fuel utilization balance index" of the method and system in this embodiment (scenario C) is the highest, indicating that its optimization model has successfully achieved the optimal allocation of high-calorific-value industrial solid waste and low-calorific-value sludge among heterogeneous units, thereby improving the overall energy conversion efficiency.

[0080] Robustness and practicality: Comparing scenario C and scenario D, it can be seen that, after considering uncertainties, the cost of the method and system in this embodiment (scenario C) is slightly higher than the ideal situation, but it successfully safeguards the carbon budget red line, proving the necessity of the robust optimization framework for ensuring the effectiveness of the actual implementation of the solution.

[0081] System implementation feasibility: This embodiment verifies the feasibility of the collaborative work of the various modules of the scheduling system, and demonstrates the complete closed-loop process from model construction and optimization solution to instruction issuance. The system has the capability for actual deployment and operation.

[0082] In summary, this embodiment fully verifies the significant advantages and practical application value of the virtual power plant dispatching method and system provided in this embodiment in improving the economic benefits of the park, ensuring the achievement of carbon targets, optimizing resource allocation, and enhancing operational robustness.

[0083] Compared with the prior art, the method provided in this embodiment can bring the following significant beneficial effects: 1. Improve the efficiency and economic benefits of comprehensive resource utilization: By optimizing the allocation of diverse solid waste fuels and intelligently adjusting the operating conditions of CCUS, the overall efficiency of the entire virtual power plant energy system is significantly improved, thereby increasing market revenue.

[0084] 2. Ensure the feasibility and controllability of the net-zero carbon target: The carbon emission budget is embedded as a hard constraint into the core of the virtual power plant dispatch, making carbon emissions a system variable that can be monitored, predicted and controlled in real time, thus ensuring the stable attainment of the park's net-zero carbon target from an operational perspective.

[0085] 3. Enhanced system flexibility and resilience: By aggregating and optimizing the scheduling of various flexible resources through virtual power plants, the park's ability to cope with internal fluctuations and external grid demands is greatly improved. Simultaneously, the distributed bar optimization framework enables virtual power plants to effectively withstand the combined impact of multiple uncertainties.

[0086] 4. Promote multi-stakeholder collaboration and data value mining: The distributed solution architecture based on virtual power plants achieves global optimization while protecting privacy, which is conducive to attracting the participation of multiple stakeholders and providing a precise data foundation for the development of carbon assets.

[0087] 5. High system integration and strong scalability: The system is modularly designed with a virtual power plant framework. The coupling between functional modules is low, which makes it easy to customize and integrate according to the specific configuration of the park, and has good engineering application prospects.

[0088] Based on the above method embodiments, this application also provides a virtual power plant electricity-carbon coordinated dispatching device for multi-element solid waste energy conversion and CCUS parks, see [link to relevant documentation]. Figure 4As shown, the device includes: a model library construction module 42, used to construct a unified aggregation model library of heterogeneous resources for virtual power plants; the unified aggregation model library of heterogeneous resources includes: a multi-element solid waste energy unit model with a unified scheduling interface for virtual power plants, a CCUS deep integration and flexibility model, and a generalized flexible resource aggregation model; an optimization scheduling model construction module 44, used to construct an electricity-carbon collaborative multi-timescale optimization scheduling model based on various models in the unified aggregation model library of heterogeneous resources, with the objective function of maximizing the total net operating revenue of the virtual power plant in the park within the scheduling cycle and with carbon emission budget as the core constraint; an uncertainty improvement module 46, used to improve the multiple uncertainties of the multi-timescale optimization scheduling model by using the sub-Bruker optimization method; and an optimal instruction generation module 48, used to perform distributed solution of the improved multi-timescale optimization scheduling model based on the alternating direction multiplier method, generate the optimal scheduling instructions for each resource, and issue the instructions through the virtual power plant intelligent control platform.

[0089] Furthermore, the aforementioned multi-element solid waste energy generation unit model includes: a power generation / heating efficiency model based on fuel calorific value and composition, a thermoelectric coupling characteristic model, and a dynamic optimization allocation model for multiple solid waste fuels among multiple units; the CCUS deep integration and flexibility model includes: a dynamic relationship model between carbon capture rate and energy consumption, and an adjustable load characteristic model; the generalized flexible resource aggregation model is a unified abstraction of electrochemical energy storage, thermal storage tanks, electric vehicle charging and swapping networks, and interruptible industrial processes into a flexible resource model with specific capacity, power, response time, cycle cost, and spatiotemporal transfer constraints.

[0090] Furthermore, in the aforementioned electricity-carbon coordinated multi-timescale scheduling model, the total net operating revenue is calculated based on electricity / heat sales revenue, ancillary service market revenue, carbon trading revenue, fuel costs, operation and maintenance costs, purchased electricity costs, and the operating energy consumption costs of CCUS. The core constraints introduce carbon flow balance and budget constraints as system constraints of equal importance to electricity balance and heat balance. Carbon flow balance and budget constraints include: real-time carbon flow balance equations and the requirement that the cumulative net carbon emissions within the scheduling cycle do not exceed the set carbon budget. The multi-timescale scheduling method includes: using virtual power plants as the executing entity, and executing on a rolling basis across three timescales. The three time scales include: the day-ahead optimization layer, the intraday rolling optimization layer, and the real-time control layer. The day-ahead optimization layer is used to optimize and determine the start-up and shutdown plans of each unit, the basic output curve, the CCUS planned capture rate curve, the energy storage baseline plan, and the flexible load baseline for the next day based on forecasts. The intraday rolling optimization layer is used to continuously optimize and adjust the output of controllable resources based on the plans determined by the day-ahead optimization layer, according to the latest ultra-short-term forecast data and actual operating deviations. The real-time control layer is used to decompose and distribute the optimal dispatch instructions to the local control systems of each resource through the virtual power plant intelligent control platform, and implement second-level frequency and power support.

[0091] Furthermore, the aforementioned uncertainty improvement module 46 is used to process uncertain parameters involved in the multi-timescale optimization scheduling model using the Wasserstein distance-based bibliometric optimization method; the uncertain parameters include: solid waste supply, photovoltaic / wind power output, and market electricity price.

[0092] Furthermore, the aforementioned optimal instruction generation module 48 is used to decompose the global optimization problem into multiple sub-problems based on the physical area boundaries or operation and management rights of resources within the park; each sub-problem contains the model, constraints, and private data of all resources within the subject or subsystem; using the alternating direction multiplier method, information exchange content and iterative process are executed based on multiple sub-problems until the convergence condition is reached, at which point the iteration stops, and the optimal scheduling instructions for each resource are obtained.

[0093] Furthermore, in the above-mentioned information exchange and iterative process based on multiple sub-problems, each sub-problem iteratively exchanges the following information with the virtual power plant center: The information reported by each sub-problem to the virtual power plant center includes: the resource scheduling plan under the current iteration step; the resource scheduling plan includes: unit output, carbon capture rate, energy storage charging and discharging power, and load adjustment amount; The information issued by the virtual power plant center to each sub-problem includes: system-level coordination signals; the coordination signals include: marginal carbon emission cost signal, system power balance signal, and Lagrange multiplier update amount.

[0094] Furthermore, the above convergence conditions include one of the following: the original residual and the dual residual are less than a set threshold; the change in the global objective function value in two adjacent iterations is less than the convergence accuracy; or the preset maximum number of iterations is reached.

[0095] The device provided in this application embodiment has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts of the device embodiment not mentioned can be referred to the corresponding content in the aforementioned method embodiment.

[0096] Based on the above method embodiments, this application also provides a virtual power plant electricity-carbon coordinated dispatch system for multi-element solid waste energy conversion and CCUS parks, see [link to relevant documentation]. Figure 5 As shown, the system includes: a server 52, an intelligent control platform 54, and an execution system 56 connected in sequence; the server 52 is used to execute the method described in the method embodiment and issue an optimal scheduling instruction to the intelligent control platform 54; the intelligent control platform 54 performs scheduling control on the execution system according to the optimal scheduling instruction; the execution system 56 includes: a multi-element solid waste unit, a CCUS coupling unit, a renewable energy management system, an energy storage system, and a flexible load.

[0097] The system provided in this application embodiment has the same implementation principle and technical effects as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the system embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0098] This application also provides a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions cause the processor to implement the above-described method. For specific implementation details, please refer to the foregoing method embodiments, which will not be repeated here.

[0099] The computer program products of the methods, apparatus, and electronic devices provided in the embodiments of this application include a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementations, please refer to the method embodiments, which will not be repeated here.

[0100] Unless otherwise specifically stated, the relative steps, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of this application.

[0101] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0102] In the description of this application, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0103] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the technical scope disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A method for coordinated electricity-carbon scheduling of a virtual power plant in a CCUS (Concentrated Environmental Protection and Research Center) park and the energy recovery from diverse solid wastes, characterized in that... The method includes: Construct a unified aggregation model library for heterogeneous resources for virtual power plants; the unified aggregation model library for heterogeneous resources includes: a multi-element solid waste energy unit model with a unified scheduling interface for virtual power plants, a CCUS deep integration and flexibility model, and a generalized flexible resource aggregation model; Based on the various models in the heterogeneous resource unified aggregation model library, an electricity-carbon collaborative multi-timescale optimization scheduling model is constructed with the objective function of maximizing the total net operating revenue of the virtual power plant in the park within the scheduling cycle and the carbon emission budget as the core constraint. The multi-time-scale optimization scheduling model is improved by using the sub-Bruker optimization method; The improved multi-timescale optimization scheduling model is solved in a distributed manner based on the alternating direction multiplier method to generate the optimal scheduling instructions for each resource, and then the instructions are issued through the virtual power plant intelligent control platform.

2. The method according to claim 1, characterized in that, The multi-element solid waste energy generation unit model includes: a power generation / heating efficiency model based on fuel calorific value and composition, a thermoelectric coupling characteristic model, and a dynamic optimization allocation model for multiple solid waste fuels among multiple units; the CCUS deep integration and flexibility model includes: a dynamic relationship model between carbon capture rate and energy consumption, and an adjustable load characteristic model; the generalized flexible resource aggregation model is a unified abstraction of electrochemical energy storage, thermal storage tanks, electric vehicle charging and swapping networks, and interruptible industrial processes into a flexible resource model with specific capacity, power, response time, cycle cost, and spatiotemporal transfer constraints.

3. The method according to claim 1, characterized in that, In the aforementioned electricity-carbon collaborative multi-timescale scheduling model, the total net operating revenue is calculated based on electricity / heat sales revenue, ancillary service market revenue, carbon trading revenue, fuel costs, operation and maintenance costs, purchased electricity costs, and the operating energy consumption costs of CCUS. The core constraints introduce carbon flow balance and budget constraints as system constraints of equal importance to power balance and heat balance; the carbon flow balance and budget constraints include: real-time carbon flow balance equations and the cumulative net carbon emissions within the scheduling cycle not exceeding the set carbon budget. The multi-timescale scheduling method includes: using a virtual power plant as the execution subject, and executing on a rolling basis at three time scales; the three time scales include: day-ahead optimization layer, intraday rolling optimization layer and real-time control layer; the day-ahead optimization layer is used to optimize and determine the start-up and shutdown plans of each unit, the basic output curve, the CCUS planned capture rate curve, the energy storage baseline plan and the flexible load baseline for the next day based on prediction. The intraday rolling optimization layer is used to adjust the output of controllable resources based on the plan determined by the day-ahead optimization layer, according to the latest ultra-short-term forecast data and actual operational deviations. The real-time control layer is used to decompose and distribute the optimal scheduling instructions to the local control systems of each resource through the virtual power plant intelligent control platform, and implement frequency and power support at the second level.

4. The method according to claim 1, characterized in that, The multi-time-scale optimization scheduling model is improved by employing a sub-Brussels bar optimization method, including: The Wasserstein distance-based bibliometric optimization method addresses the uncertain parameters involved in the multi-timescale optimization scheduling model; these uncertain parameters include: solid waste supply, photovoltaic / wind power output, and market electricity price.

5. The method according to claim 1, characterized in that, The improved multi-timescale optimization scheduling model is solved in a distributed manner based on the alternating direction multiplier method to generate the optimal scheduling instructions for each resource, including: Based on the physical boundaries or operational management rights of resources within the park, the global optimization problem is decomposed into multiple sub-problems; each sub-problem contains the model, constraints, and private data of all resources within that subject or subsystem. The alternating direction multiplier method is adopted to perform information exchange and iterative processes based on multiple subproblems until the convergence condition is met, at which point the iteration stops and the optimal scheduling instructions for each resource are obtained.

6. The method according to claim 5, characterized in that, In the process of exchanging information and iterating through multiple sub-problems, each sub-problem iteratively exchanges the following information with the virtual power plant center: The information reported by each sub-problem to the virtual power plant center includes: the resource scheduling plan under the current iteration step; the resource scheduling plan includes: unit output, carbon capture rate, energy storage charging and discharging power, and load adjustment amount; The information sent by the virtual power plant center to each sub-problem includes: system-level coordination signals; the coordination signals include: marginal carbon emission cost signals, system power balance signals, and Lagrange multiplier update quantities.

7. The method according to claim 5, characterized in that, The convergence condition includes one of the following: The original residual and the dual residual are both less than a set threshold. The change in the global objective function value between two consecutive iterations is less than the convergence accuracy. The preset maximum number of iterations has been reached.

8. A multi-element solid waste energy conversion and CCUS park virtual power plant electricity-carbon co-dispatch device, characterized in that, The device includes: The model library construction module is used to build a unified aggregation model library of heterogeneous resources for virtual power plants. The unified aggregation model library of heterogeneous resources includes: a multi-element solid waste energy unit model with a unified scheduling interface for virtual power plants, a CCUS deep integration and flexibility model, and a generalized flexible resource aggregation model. The optimization scheduling model construction module is used to construct an electricity-carbon collaborative multi-timescale optimization scheduling model based on various models in the heterogeneous resource unified aggregation model library. The objective function is to maximize the total net operating revenue of the virtual power plant in the park within the scheduling cycle, and the core constraint is the carbon emission budget. The uncertainty improvement module is used to improve the multiple uncertainties of the multi-time-scale optimization scheduling model by employing the sub-Bruker optimization method. The optimal instruction generation module is used to perform distributed solution of the improved multi-timescale optimization scheduling model based on the alternating direction multiplier method, generate the optimal scheduling instructions for each resource, and issue the instructions through the virtual power plant intelligent control platform.

9. A multi-element solid waste energy conversion and CCUS park virtual power plant electricity-carbon coordinated dispatch system, characterized in that, The system includes: a server, an intelligent control platform, and an execution system connected in sequence; the server is used to execute the method as described in any one of claims 1-8, and to issue an optimal scheduling instruction to the intelligent control platform; the intelligent control platform performs scheduling control on the execution system according to the optimal scheduling instruction; the execution system includes: a multi-element solid waste unit, a CCUS coupled unit, a renewable energy management system, an energy storage system, and a flexible load.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when invoked and executed by a processor, cause the processor to perform the method according to any one of claims 1 to 7.

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