Optimized scheduling method and device for integrated energy system

Abstract

The invention provides an integrated energy system optimization scheduling method and device, and relates to the technical field of integrated energy system scheduling. Firstly, a comprehensive energy system architecture considering carbon-electricity-storage coupling is constructed, participants of a double-layer game are explained, an objective function of an upper-layer leader and lower-layer participants is constructed, the earnings of the upper-layer leader are maximized, and the cost of the lower-layer participants is minimized; then corresponding mathematical models are established for wind and light prediction, an energy storage side and a load side, and constraint conditions are set; combining the carbon market with the electricity market according to the stepped carbon transaction, so that the carbon cost is considered in the operation process of the system; and finally, verifying the existence of a unique equilibrium solution of the master-slave game, converting a target function of a lower-layer model to an upper layer according to a KKT condition, and solving the overall model by using CPLEX to obtain an optimal scheduling scheme of the system. The method has both calculation efficiency and solution precision, greatly reduces the model complexity, and ensures that the system operation cost is minimized and the carbon emission is obviously reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy system scheduling, and in particular to an integrated energy system optimization scheduling method and device. Background Art

[0002] With the deepening implementation of the "dual carbon" strategy, the penetration rate of new energy sources, represented by wind power and photovoltaics, in the power system has increased significantly. However, the randomness and volatility of new energy output has led to high wind and solar power curtailment rates in the system. Furthermore, traditional power market mechanisms have failed to effectively connect carbon emission constraints with economic benefits, creating a contradiction between low-carbon goals and economic operations.

[0003] Although existing research has made some progress in energy storage modeling, tiered carbon trading mechanisms, and source-storage coordinated optimization, there are still shortcomings: most models are limited to single-objective optimization and lack a dynamic coordinated mechanism for carbon-electricity-storage multi-market coupling; energy storage systems lack flexibility in participating in the electricity market, and traditional time-of-use bidding mechanisms are difficult to reflect the dynamic value of energy storage; existing scheduling methods are mostly based on static games or single-layer optimization frameworks, which cannot characterize the dynamic game relationship between integrated energy service providers and virtual power plants, resulting in a deviation between the market equilibrium solution and actual operating needs.

[0004] In addition, current research does not adequately consider the complexity and uncertainty of multi-agent interactions in new power systems. For example, the dynamic electricity price game between integrated energy service providers and multiple virtual power plants, the adjustable potential of load-side demand response, and the impact of carbon trading costs on scheduling decisions all require more sophisticated modeling and algorithm optimization to achieve coordination.

[0005] To address the above issues, there is an urgent need for a comprehensive scheduling method that can coordinate carbon constraints, energy storage flexibility, and multi-agent games. While ensuring the economy of the system, it can also improve the efficiency of new energy consumption and low-carbon performance, and provide technical support for the market-oriented operation of new power systems. Summary of the Invention

[0006] The purpose of the present invention is to provide a comprehensive energy system optimization scheduling method and device, which is used to solve the problems that although the existing technology and existing research have made certain progress in energy storage modeling, step-by-step carbon trading mechanism and source-storage coordinated optimization, there are still deficiencies, and can achieve the optimal economy and minimum carbon emission operation of the power system.

[0007] In order to achieve the above objectives, the present invention provides a method for optimizing and scheduling an integrated energy system, comprising: When new energy and energy storage are integrated into the power system, an integrated energy system architecture is constructed; the integrated energy system architecture includes the power grid, integrated energy service providers, and at least one virtual power plant; Obtain new energy output forecast results and build an energy storage model. Based on the new energy output forecast results and the energy storage model, establish a collaborative optimization scheduling model between the upper-level integrated energy service provider and the lower-level virtual power plant, determine the objective function, and set constraints; Embed the carbon trading market into the integrated energy system architecture using a tiered carbon trading model, taking into account the transferability and curtailment of loads, and adopting demand response where loads are concentrated; With integrated energy service providers as leaders setting electricity prices and virtual power plants as followers responding to dispatch, a Stackelberg game model with one master and multiple followers is established. It is proven that the Stackelberg game model has a unique equilibrium solution, achieving the optimal synergy between the global economy and low-carbon goals. The KKT condition is used to transform the lower-level virtual power plant optimization problem into the upper-level integrated energy service provider constraints. A single-level linear programming model is constructed and solved using the CPLEX solver to output the power system scheduling strategy.

[0008] According to a comprehensive energy system optimization and scheduling method provided by the present invention, the new energy output prediction result includes wind and solar power prediction values; Obtain new energy output forecast results, including: The variational mode decomposition algorithm is used to perform variational mode decomposition on the historical wind and solar power, decomposing the complex signal into multiple sparse intrinsic mode function components. A CNN-BiLSTM model is constructed, and the intrinsic mode function components are input as a combination of wind direction angle, wind speed, and temperature. The Adam optimizer is used to train the CNN-BiLSTM model. After training, the predicted intrinsic mode function components are superimposed to obtain the predicted wind and solar power value.

[0009] According to a comprehensive energy system optimization scheduling method provided by the present invention, energy storage includes pumped storage power stations, electrochemical energy storage, hydrogen energy storage and compressed air energy storage.

[0010] According to an integrated energy system optimization scheduling method provided by the present invention, the objective function includes maximizing the revenue of the upper-level integrated energy service provider and minimizing the cost of the lower-level virtual power plant.

[0011] According to a comprehensive energy system optimization scheduling method provided by the present invention, the constraints include:

[0012]

[0013]

[0014] Where, are the operating powers of photovoltaic power generation, wind power generation and thermal power generation in the tth time period respectively; , , , are the operating powers of the pumped storage power station, electrochemical energy storage, hydrogen energy storage and compressed air energy storage in the tth time period respectively; p Load,t is the power of the load in the tth time period.

[0015] According to the present invention, a method for optimizing and dispatching an integrated energy system is provided. This method uses a tiered carbon trading system to embed a carbon trading market into the integrated energy system architecture. The method also considers the transferability and curtailment of loads and adopts demand response at load concentration points. The method includes: According to the preset standards, the carbon quota for thermal power generation is determined as follows:

[0016] Where, Carbon quota for thermal power generation, is the carbon quota coefficient, is the operating power of the new energy unit in time period t; According to the preset standards, the carbon emission coefficient per unit of electricity generated by thermal power is determined, and the actual carbon emissions are calculated as follows:

[0017] Where, is the actual carbon emissions from thermal power generation, is the carbon emission coefficient per unit of electricity generated by thermal power, is the unit time length; Then the actual carbon trading amount in the carbon trading market is obtained as follows:

[0018] The dynamic parameter adjustment mechanism for carbon market transaction carbon prices is determined as follows:

[0019] In the formula, c is the carbon price in carbon market, c base is the benchmark carbon price, is the permeability sensitivity coefficient;

[0020] Where, d is the step interval length, d 0 is the length of the reference interval;

[0021] Where, is the price increase factor, is the initial increase, is the reinforcing factor; Then the step-by-step carbon trading model is constructed as follows:

[0022] Where, Carbon trading costs for the electricity system; When the user aggregator at the load aggregation location participates in demand response, the charge of the user aggregator is:

[0023] Where, is the fixed load in time period t, is the translatable load in time period t; where the translatable load is:

[0024] Where, It represents the basic amount of load that can be translated in the tth time period, represents the price response elasticity coefficient, represents the basic electricity purchase price in time period t, Indicates the maximum load that can be translated within T time periods.

[0025] According to a comprehensive energy system optimization scheduling method provided by the present invention, the Stackelberg game model is:

[0026] Among them, the integrated energy service provider and the virtual power plant are the two participants of the Stackelberg game model, and the participant set is represented as ; The strategic object of the leader integrated energy service provider is the purchase price and sales price of electricity per unit time, which is expressed in the form of a vector: The strategy objects of the follower virtual power plant are the output of thermal power generation, wind power generation, photovoltaic power generation and the energy storage strategy of pumped storage power station, electrochemical energy storage, hydrogen energy storage and compressed air energy storage, which are expressed as vectors ; The goal of integrated energy service providers is to maximize profits by optimizing electricity pricing strategies, while the goal of virtual power plants is to minimize costs by optimizing resource scheduling. In the Stackelberg game model, equilibrium is achieved when all followers make their own optimal decisions based on the leader's decision, and the leader also adjusts its own decision to the optimal level based on the follower's decision feedback. represents the equilibrium solution of the master-slave game in the Stackelberg game model, then:

[0027] In the equilibrium solution, any participant cannot increase its profit by unilaterally adjusting its strategy.

[0028] According to a method for optimizing and scheduling an integrated energy system provided by the present invention, the conditions that must be met for a unique equilibrium solution to exist in a Stackelberg game model are: the strategy sets of the leader and the followers are non-empty, compact convex sets; when the leader's strategy is determined, all followers have a unique optimal solution; when the leader's strategy is determined, the leader has a unique optimal solution.

[0029] According to the present invention, a comprehensive energy system optimization and scheduling method is provided. The KKT condition is used to transform the lower-level virtual power plant optimization problem into the upper-level comprehensive energy service provider constraint. A single-level linear programming model is constructed and solved using the CPLEX solver to output a power system scheduling strategy, including: The cost function of the virtual power plant is transformed into an augmented Lagrangian function, and the KKT equilibrium condition and complementary relaxation condition are set. The CPLEX solver is used to maximize the profit function of the upper-level integrated energy service provider. Its constraints include not only the main constraints of the upper-level planning and the constraints of the lower-level virtual power plant, but also the complementary relaxation conditions of the lower-level optimization problem are transformed into equivalent algebraic constraints through KKT condition transformation, and the coordinated optimization of the two-level decision-making is realized simultaneously, and the power system dispatching strategy that meets the global optimality requirements is output.

[0030] In a second aspect, the present invention provides a comprehensive energy system optimization and scheduling device, comprising: A construction unit for constructing an integrated energy system architecture when new energy and energy storage are connected to the power system; the integrated energy system architecture includes a power grid, an integrated energy service provider, and at least one virtual power plant; Establish a unit to obtain renewable energy output forecast results and build an energy storage model. Based on the renewable energy output forecast results and the energy storage model, establish a collaborative optimization scheduling model between the upper-level integrated energy service provider and the lower-level virtual power plant, determine the objective function, and set constraints; The response unit is used to embed the carbon trading market into the integrated energy system architecture using a tiered carbon trading model, taking into account the transferability and curtailment of loads, and adopting demand response where loads are concentrated; An optimization unit is used to establish a one-master, multiple-slave Stackelberg game model with integrated energy service providers as leaders setting electricity prices and virtual power plants as followers responding to dispatch requests. The model also proves that a unique equilibrium solution exists, achieving optimal coordination between the global economy and low-carbon goals. The output unit is used to transform the lower-level virtual power plant optimization problem into the upper-level integrated energy service provider constraints using KKT conditions, construct a single-level linear programming model, solve it using the CPLEX solver, and output the power system scheduling strategy.

[0031] The present invention has at least the following technical effects: The present invention provides a method and device for optimizing and dispatching an integrated energy system. First, an integrated energy system architecture considering carbon-electricity-storage coupling is constructed, and the participants in the two-layer game are explained. The objective functions of the upper-layer leader and the lower-layer participants are constructed. The upper-layer leader maximizes benefits, and the lower-layer participants minimize costs. Then, corresponding mathematical models are established for wind and solar forecasting, energy storage side, and load side, and constraints are set. Then, the carbon market is combined with the electricity market based on a stepped carbon trading system, so that carbon costs are considered during system operation. Finally, the existence of a unique equilibrium solution of the master-slave game is verified, and the objective function of the lower-layer model is converted to the upper layer according to the KKT condition. The overall model is solved using CPLEX to obtain the optimal scheduling scheme for the system. The present invention combines computational efficiency and solution accuracy, significantly reduces model complexity, and ensures that system operating costs are minimized and carbon emissions are significantly reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0033] In the attached figure: Figure 1 This is a simplified diagram of a regional power grid structure in an embodiment of the present invention; Figure 2 This is a load size data diagram for each time period within a day in an embodiment of the present invention; Figure 3 This is a graph of grid electricity price data for each time period within a day in an embodiment of the present invention; Figure 4 : This is a graph showing the energy storage SOE regulation cost curve in an embodiment of the present invention; Figure 5 This is a diagram showing the overall power situation of the source, grid, load and storage of VPP1 in an embodiment of the present invention; Figure 6 1 is a diagram showing the overall power situation of the source, grid, load and storage of VPP2 in an embodiment of the present invention; Figure 7 1 is a diagram showing the overall power situation of the source, grid, load and storage of VPP3 in an embodiment of the present invention; Figure 8 1 is a diagram of the scheduling strategy of IESP in an embodiment of the present invention; Figure 9 This is a diagram of the integrated energy system architecture of the present invention; Figure 10 This is a graph showing the energy storage SOE regulation cost curve of the present invention; Figure 11It is a flow chart of the integrated energy system optimization scheduling method of the present invention. DETAILED DESCRIPTION

[0034] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0035] The following will describe some embodiments of the present invention in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.

[0036] See also Figure 11 The embodiment of the present invention provides a method for optimizing the scheduling of an integrated energy system based on a master-slave game and considering the coupling of carbon, electricity and storage, including the following steps: Step 1: When new energy and energy storage are connected to the power system, an integrated energy system architecture is constructed; the integrated energy system architecture includes a power grid, an integrated energy service provider, and at least one virtual power plant; Specifically, based on the characteristics of the new power system, a two-tier architecture is constructed with virtual power plants as units and integrated energy service providers as the core hub.

[0037] It should be noted that, in view of the characteristics of the new power system, especially the background of high penetration rate of new energy and energy storage access, the present invention adopts a comprehensive energy system architecture with a virtual power plant (VPP) with "source-load-storage" as the main feature and an integrated energy service provider (IESP) as the bridge. Through this architecture, the efficient development and utilization of new energy can be promoted, and the randomness and uncertainty of its output can be effectively smoothed out.

[0038] The integrated energy system architecture proposed in this invention is not limited to a single VPP, but uses the power market as a link to organically integrate multiple VPPs through a collaborative and interconnected power network to achieve economical and efficient energy supply and scientific and reasonable energy utilization. The specific architecture is as follows: Figure 9 shown.

[0039] The IESP of this invention is based on the electricity sales company model in the electricity market. Acting as a bridge between different VPPs, it optimizes electricity purchase and sales prices based on supply and demand, purchasing electricity from VPPs with oversupply and selling it to VPPs with insufficient supply, thereby generating revenue. This model provides distributed VPPs with more flexible pricing strategies than traditional electricity markets, effectively promoting their participation in market competition and encouraging small and medium-sized community enterprises to scientifically dispatch loads.

[0040] During energy trading, IESPs must address risks such as supply-demand imbalances and price fluctuations. Specifically, if the total generation capacity of their VPP clusters fails to meet load demand, the power system operator will trigger the power shortfall compensation mechanism, forcing the IESP to purchase power from the main grid at inflated spot market prices. This rigid power replenishment behavior directly impacts trading processes and can lead to operational losses.

[0041] In order to promote the realization of the "dual carbon" goals as scheduled, my country's carbon market trading mechanism is being actively promoted. However, my country's existing electricity market mechanism fails to fully consider the carbon emissions of the units, resulting in a disconnect between the economic benefits of the power system and its low-carbon attributes. Based on this, the present invention embeds the carbon trading market into the existing integrated energy system architecture to achieve effective market linkage of carbon-electricity-storage coupling, and constructs a carbon-electricity-storage market joint operation mechanism. By converting the carbon emissions of the units into power generation costs and incorporating them into electricity market considerations, this mechanism has improved the utilization rate of new energy to a certain extent and reduced dependence on traditional thermal power generation.

[0042] Considering the lack of competitiveness of energy storage systems under traditional time-of-use bidding mechanisms, this paper adopts a flexible energy storage state operation mode. By flexibly constraining the final state of energy (SOE) of energy storage, it directly formulates a mechanism for energy storage to participate in market competition based on SOE demand. This method can more effectively reflect the value of energy storage systems and accurately reflect trading intentions. The specific mechanism is detailed in step 3.3.

[0043] Furthermore, considering the subjective initiative of the load side, this invention aggregates a group of users with demand response (DR) capabilities and implements cluster management. While ensuring the rigidity of fixed load requirements, this invention further considers the temporal and spatial mobility of load and its potential for curtailment, establishing a certain proportion of transferable load. Based on the day-ahead time-of-use electricity price published by the IESP, users optimize their load demand hour by hour, achieving an interactive game between demand-side resources and wholesale market prices.

[0044] Step 2: Obtain the new energy output forecast results and build an energy storage model. Based on the new energy output forecast results and the energy storage model, establish a collaborative optimization scheduling model for the upper-level integrated energy service provider and the lower-level virtual power plant, determine the objective function, and set constraints; Specifically, the new energy output forecast result includes the wind and solar power forecast value. Step 2 includes the following steps: Step 2.1: Use the Variational Mode Decomposition (VMD) algorithm to perform variational mode decomposition on historical wind and solar power, decomposing the complex signal into multiple sparse intrinsic mode function (IMF) components. A CNN-BiLSTM model (a hybrid architecture combining a convolutional neural network (CNN) and a bidirectional long short-term memory network (BiLSTM)) is constructed, combining the IMF components with external features (such as wind direction, wind speed, and temperature). Finally, the CNN-BiLSTM model is trained using the Adam (Adaptive Moment Estimation) optimizer. After training, the predicted IMF components are combined to obtain the final wind and solar power forecast. Subsequent scheduling optimization will be performed based on the wind and solar power forecast obtained in this step.

[0045] In the integrated energy system architecture established in Step 2.2 and Step 1, energy storage includes pumped hydro, electrochemical energy storage, hydrogen energy storage, and compressed air energy storage. This paper considers the different physical characteristics and application scenarios of each of these four energy storage types, developing detailed modeling to fully integrate energy storage into the entire scheduling process and maximize its role in peak load shifting and valley filling.

[0046] Step 2.3: As the market leader, the upper-level integrated energy service provider formulates a pricing strategy based on the supply and demand relationship. The optimization goal is to maximize the profit, which can be expressed as:

[0047] Where, F IESP is the income of the upper-level integrated energy service provider; T is the total number of time periods, and N is the number of VPPs; is the electricity sales revenue of IESP to the i-th VPP in time period t; is the cost of electricity purchased by IESP from the i-th VPP in time period t; is the income from electricity sales from IESP to the grid in time period t, is the cost of electricity purchased from the grid by IESP in time period t. The above items can be expressed as:

[0048]

[0049]

[0050]

[0051] Where, and They represent the electricity sales and purchase prices of IESP to the i-th VPP in the t-th time period, and They represent the power sold and purchased by IESP to the i-th VPP in the t-th time period, and They represent the electricity price sold by IESP to the grid and the electricity price purchased from the grid in time period t, and They represent the power sold to and purchased from the grid by IESP in time period t.

[0052] To maintain market structure stability, direct transaction channels between VPP and the main grid must be blocked through transaction boundary constraints. The IESP buying (selling) price should be kept slightly higher (lower) than the grid market price:

[0053] At the same time, it is also necessary to ensure the power balance of IESP at all times:

[0054] In step 2.4, the lower-level virtual power plant acts as a follower and constructs an optimization scheduling model under a given electricity price strategy. It comprehensively considers a series of constraints on the generation side, energy storage side, and load side, builds a decision-making system for the coordinated optimization of generation-energy storage-load, and establishes an optimization model with the goal of minimizing cost, which can be expressed as:

[0055] Where, is the cost of the lower virtual power plant, is the cost of purchasing electricity from the integrated energy service provider in time period t; is the electrochemical energy storage operating cost in time period t; is the hydrogen storage cost in time period t; is the operating cost of the pumped storage power station in time period t; is the compressed air energy storage operating cost in time period t; is the photovoltaic power generation cost in the tth time period; is the cost of wind power generation in time period t; is the thermal power generation cost in the tth time period; is the electricity sales revenue to the integrated energy service provider in time period t. The above items can be expressed as:

[0056]

[0057] Where, and are the electricity purchase and electricity sales prices of the integrated energy service provider in time period t, and are the electricity purchase and sales power of the integrated energy service provider in time period t respectively.

[0058]

[0059]

[0060]

[0061]

[0062] Where, is the unit operating cost of the pumped storage power station, is the start-up and shutdown cost of the pumped storage unit, , , Represent the operating costs per unit power of batteries, hydrogen energy storage and compressed air energy storage respectively; , , , They are respectively represented as the operating power of pumped storage, energy storage battery, hydrogen energy storage and compressed air energy storage in the tth time period; is the start / stop status of the i-th pumped storage unit in the t-th time period, 1 indicates working and 0 indicates shutdown; Indicates the operating efficiency of the energy storage battery.

[0063]

[0064] Where, Indicates the operating cost of photovoltaic power generation per unit power, Indicates the operating cost of wind power generation per unit power, Indicates the operating cost of thermal power generation per unit power, are the operating powers of photovoltaic, wind power and thermal power in the tth time period respectively.

[0065] For each VPP system operating normally, the power balance constraints must be met:

[0066] p Load,t is the power of the load in the tth time period.

[0067] For the four energy storage elements in the model, the following constraints must be met.

[0068] The modeling of pumped storage power stations mainly focuses on the operation modeling of the reservoir. Therefore, the storage capacity constraints of pumped storage power stations must meet the following requirements:

[0069] Where, is the maximum value of the difference in reservoir capacity at the beginning and end of the day, is the number of generators in the hydropower station, and Represent the maximum and minimum values ​​of the reservoir capacity, The maximum number of state switches allowed per day.

[0070] For electrochemical energy storage, the battery must maintain its capacity within a constraint during the charge and discharge cycle, namely:

[0071] Where, is the total capacity of the battery, is the battery charge at the initial moment, It indicates the total charge and discharge capacity of the battery after the tth time period. S ocmax The maximum percentage of remaining battery power. S ocmin The minimum percentage of remaining battery charge.

[0072] For hydrogen energy storage, the fuel cell, electrolyzer, and hydrogen storage tank must always meet the following constraints during the scheduling process:

[0073] Where, 、 are the upper and lower limits of fuel cell power, 、 are the upper and lower limits of electrolytic cell power, 、 are the upper and lower limits of the hydrogen storage tank’s state of charge, 、 are the upper and lower pressure limits of the hydrogen storage tank, is the pressure inside the hydrogen storage tank, is the maximum allowable rate of pressure change.

[0074] For compressed air energy storage, the amount of energy in the gas tank at each moment The pressure can be expressed as:

[0075] Where, is the pressure in the gas tank at time period t, , They are the minimum and maximum capacity of the gas tank, is the volume of the gas tank, 、 It is a 0-1 variable. When the compressor is working, The value is 1, when the turbine is working, The value is 1. k t is the formation permeability, reflecting the dynamic impact of gas leakage on pressure. is the air temperature of the gas tank, is the compressor air mass flow rate, is the turbine air mass flow rate. R g is the gas constant and remains unchanged during the calculation process.

[0076] Step 3: Embed the carbon trading market into the integrated energy system architecture using a tiered carbon trading model, taking into account the transferability and curtailment of loads, and adopting demand response where loads are concentrated; Specifically, step 3 includes the following steps: Step 3.1: Comprehensively consider the relevant provisions of national standards and determine the carbon emission quota coefficient of thermal power units and the carbon emission quota of thermal power units. for:

[0077] Where, It is the carbon quota of thermal power generation (also known as thermal power units or coal-fired units). is the carbon quota coefficient, is the operating power of the new energy unit in the tth time period.

[0078] According to the relevant provisions of national standards, the carbon emission coefficient per unit of electricity of coal-fired units is determined, and the actual carbon emissions are calculated based on the obtained carbon emission quota:

[0079] Where, is the actual carbon emissions of coal-fired units, is the carbon emission coefficient per unit electricity of coal-fired units, The unit time length.

[0080] According to the above calculation method of carbon emissions and carbon quotas, the actual carbon trading amount in the carbon trading market is obtained:

[0081] Where, Indicates the actual carbon trading amount in the carbon trading market. , you need to purchase carbon quotas; if , the remaining quotas can be sold.

[0082] In order to respond to the national dual carbon call, further reduce carbon emissions and increase the new energy consumption rate, the present invention adopts a dynamically adjusted step-by-step carbon trading mechanism to convert carbon dioxide emissions into costs, realize the combination of carbon market and electricity market, promote virtual power plants to reduce thermal power generation, and improve the new energy consumption capacity.

[0083] The dynamic parameter adjustment mechanism for carbon market transaction price negotiation is as follows:

[0084] Where, Carbon price for carbon market transactions, is the benchmark carbon price, It is the penetration sensitivity coefficient, which reduces the carbon price in the scenario with high proportion of new energy in real time.

[0085]

[0086] Where d is the length of the step interval, d 0 is the length of the reference interval.

[0087]

[0088] Where, is the price increase factor, is the initial increase, It is a policy strengthening factor and will be adjusted upward dynamically along with the national emission reduction targets.

[0089] Based on the national standardization technical document GB / T 32150-2015 "General Rules for Accounting and Reporting Greenhouse Gas Emissions from Industrial Enterprises", the IPCC greenhouse gas emission factor database, and the basic parameters such as the carbon trading benchmark price confirmed above, a step-by-step carbon trading model is constructed as follows:

[0090] Where, The carbon trading costs for the power system.

[0091] Step 3.2: Demand response balances power supply and demand and optimizes resource allocation by incentivizing users to adjust their electricity consumption. For effective market participation, establishing an accurate load model is crucial. When a user aggregator at a load aggregation location participates in demand response, the user aggregator's charge consists of fixed charge and shiftable charge, which can be expressed as:

[0092] Where, is the fixed load in the tth time period. This part of the load is the rigid load of the user and needs to be highly stable to meet the needs of the user's daily life. The translatable load in the tth time period can be expressed as:

[0093] Where, It represents the basic amount of load that can be translated in the tth time period, represents the price response elasticity coefficient, represents the basic electricity purchase price in time period t, Indicates the maximum load that can be translated within T time periods.

[0094] It should be noted that energy storage plays a crucial role in the market due to its ability to store and flexibly dispatch electricity. However, existing research often employs a rigid constraint modeling paradigm with a fixed boundary energy state. This static energy strategy fails to consider the spatiotemporal coupling between energy storage charging and discharging costs and spot price signals, leading to the loss of cross-timescale arbitrage opportunities. Therefore, this paper employs a flexible SOE constraint approach to more effectively reflect the market value of energy storage systems and accurately reflect their trading intentions.

[0095] In the model, this strategy is expressed as a relaxed constraint on the final SOE of energy storage. Taking electrochemical energy storage as an example, the traditional constraint is usually expressed as:

[0096] In the flexible SOE operation mode, the constraints are flexible:

[0097] Where, and Respectively represent the upper and lower limits of energy storage SOE. Compared with the traditional constraints, the improved With more flexibility, energy storage systems can participate in peak load and frequency regulation according to market conditions, further increasing the potential for revenue.

[0098] Energy storage participates in the market and provides peak load regulation services in order to obtain certain economic benefits. This benefit can be represented by the SOE regulation cost curve, such as Figure 10 As shown. The final SOE of energy storage preset is the optimal energy state point ( If the energy storage causes SOE to deviate from the optimal state point due to the transaction, the energy storage will be compensated according to the degree of deviation. For example, the final result falls within During this period, the market must follow The unit price compensates for the reduced SOE. Depending on the situation, the curve can be linear, stepped, or V-shaped. This operational model embodies the process by which energy storage receives compensation for adjustments to the target SOE, achieving deep integration between energy storage and the electricity market.

[0099] Step 4: With IESP as the leader to set electricity prices and VPP as the follower to respond to dispatch, a Stackelberg game model with one master and multiple followers is established. It is proved that the model has a unique equilibrium solution to achieve the optimal coordination between the global economy and low-carbon goals.

[0100] Specifically, within the framework constructed by this invention, there is a close interaction between the scheduling decision optimization of virtual power plants and the electricity pricing strategies of integrated energy service providers. Specifically, the scheduling optimization of VPPs depends on the purchase and sales prices of electricity set by IESPs, and the optimization results of VPPs in turn have an impact on the electricity price adjustments of IESPs. This collaborative optimization process between electricity prices and scheduling embodies the typical characteristics of iterative dynamic games. Therefore, this invention uses IESP as the leader and VPP as the follower, and establishes a Stackelberg game model as follows:

[0101] The Stackelberg game model of the present invention can be divided into three important factors, namely participants, strategy sets and goals, which can be specifically expressed as: (1) Participants: IESP and VPP are two participants in this game model. The participant set can be expressed as .

[0102] (2) Strategy set: The strategy object of the leader IESP is the purchase price and sales price of electricity at a unit time, which can be expressed in the form of a vector as The strategy of the follower VPP is based on the output of thermal power generation, wind power generation, and photovoltaic power generation, and the energy storage strategy of pumped storage power stations, electrochemical energy storage, hydrogen energy storage, and compressed air energy storage, expressed as a vector .

[0103] (3) Objective: The objective is an indicator to measure the decision-making effect of each participant. In this invention, the objective of IESP is to maximize profits through electricity price strategy optimization. The specific calculation process can be known from the objective function; the objective of VPP is to minimize costs through resource scheduling optimization. The specific calculation process can be known from the objective function.

[0104] In the Stackelberg game model, when all followers make their own optimal decisions based on the leader's decision, and the leader also adjusts its own decision to the optimal level based on the follower's decision feedback, the game reaches equilibrium. To represent the equilibrium solution of the master-slave game in the model, it must satisfy:

[0105] In the equilibrium solution, any participant cannot improve their benefits by unilaterally adjusting their strategy. Before finding the equilibrium solution, we need to prove its existence and uniqueness: Theorem: In a master-slave game, a unique equilibrium solution exists if the following conditions are met: (1) The strategy sets of the leader and the follower are non-empty, compact, convex sets; (2) When the leader's strategy is determined, all followers have a unique optimal solution; (3) When the leader’s strategy is determined, there is a unique optimal solution for the leader.

[0106] Proof: In the game model established by this invention, the strategy set of each participant is and All are subject to linear constraints, and obviously meet the requirements of non-empty, compact and convex sets, satisfying condition (1).

[0107] For the lower-tier VPP, the objective function includes 9 parts. and electricity sales revenue , power generation side costs and four energy storage costs They are all linear functions. The combination of linear functions maintains quasi-convexity on convex sets, and there are no multiple regions of equivalent optimal solutions. Therefore, based on a given electricity price, the objective function of the lower-level VPP is a quasi-convex function, and there is a unique optimal solution, satisfying condition (2).

[0108] Similarly, it can be proved that the objective function of the upper-level IESP is also composed of quasi-convex functions, and there exists an optimal solution that satisfies condition (3).

[0109] Therefore, the Stackelberg model established by the present invention has a unique equilibrium solution.

[0110] Step 5: Using the Karush-Kuhn-Tucker (KKT) conditions, the lower-level VPP optimization problem is transformed into upper-level IESP constraints. A single-level linear programming model is constructed and solved using the CPLEX solver (an advanced optimization tool for complex optimization problems that supports a variety of mathematical models, including linear programming, mixed integer programming, and nonlinear programming). The resulting power system dispatch strategy is output. This achieves the dual goals of minimizing power system costs and reducing carbon emissions.

[0111] Specifically, considering the large number of variables and constraints in the model, using heuristic algorithms may result in excessively long solution times or lead to local optimal solutions. Since the price is used as a constant in the calculation of the lower-level VPP model after the upper-level leader formulates the pricing strategy, making the lower-level model linear, the KKT condition can be used to transform the lower-level model into the constraints of the upper-level model, thereby solving the single-level model. The specific solution process is as follows: First, virtual power plants i The cost function is transformed into the augmented Lagrangian function, as shown in formula (1), where is the Lagrange multiplier of the equation in the KKT condition, is the Lagrange multiplier of the inequality in the KKT condition. The KKT equilibrium condition is shown in Equation (2), and the complementary relaxation condition is shown in Equation (3). In Equation (3), the symbol “⊥” indicates that the equations before and after the symbol are valid, and the multiplication of the equations before and after the symbol is 0. For example: Expressed as:

[0112]

[0113]

[0114] Formula (1):

[0115] Formula (2):

[0116] Formula (3):

[0117] After setting the KKT equilibrium conditions and complementary relaxation conditions, the CPLEX solver is used to maximize the upper-level IESP benefit function. Its constraints include not only the main constraints of the upper-level planning and the constraints of the lower-level virtual power plant, but also the complementary relaxation conditions of the lower-level optimization problem are transformed into equivalent algebraic constraints through KKT condition transformation. The coordinated optimization of the two-level decision-making can be realized simultaneously, and the final output is a power system dispatching strategy that meets the global optimality requirements.

[0118] Based on the same inventive concept, another embodiment of the present invention provides an integrated energy system optimization and scheduling device, which corresponds to the method of the above embodiment and includes: A construction unit for constructing an integrated energy system architecture when new energy and energy storage are connected to the power system; the integrated energy system architecture includes a power grid, an integrated energy service provider, and at least one virtual power plant; Establish a unit to obtain renewable energy output forecast results and build an energy storage model. Based on the renewable energy output forecast results and the energy storage model, establish a collaborative optimization scheduling model between the upper-level integrated energy service provider and the lower-level virtual power plant, determine the objective function, and set constraints; The response unit is used to embed the carbon trading market into the integrated energy system architecture using a tiered carbon trading model, taking into account the transferability and curtailment of loads, and adopting demand response where loads are concentrated; An optimization unit is used to establish a one-master, multiple-slave Stackelberg game model with integrated energy service providers as leaders setting electricity prices and virtual power plants as followers responding to dispatch requests. The model also proves that a unique equilibrium solution exists, achieving optimal coordination between the global economy and low-carbon goals. The output unit is used to transform the lower-level virtual power plant optimization problem into the upper-level integrated energy service provider constraints using KKT conditions, construct a single-level linear programming model, solve it using the CPLEX solver, and output the power system scheduling strategy.

[0119] The following is a specific embodiment of the present invention.

[0120] In order to verify the effectiveness of the proposed integrated energy system scheduling optimization method based on master-slave game considering carbon-electricity-storage coupling, a simplified model of the local power grid structure is constructed based on the data of a certain province's regional power grid. The architecture is as follows: Figure 1 As shown. There is an upper-level energy supplier and three lower-level virtual power plants. The upper-level energy supplier can exchange energy with the main grid, or purchase and sell electricity from different virtual power plants to make a profit. WT is a wind turbine with a rated power of 60kW, PV is a photovoltaic unit with a rated power of 250kW, and TP is a thermal power unit with a rated power of 300kW. For pumped storage systems, For electrochemical energy storage systems, For hydrogen energy storage system, The VPP1 is equipped with thermal power, wind power, photovoltaic power, pumped storage and compressed air energy storage systems, VPP2 is equipped with thermal power, wind power, photovoltaic power, electrochemical energy storage and hydrogen energy storage systems, and VPP3 is equipped with thermal power, wind power and electrochemical energy storage systems. Load is the load side. The load size of the three VPPs in different time periods on a typical day is as follows: Figure 2 shown.

[0121] In this embodiment, the total number of time periods T is 24, and the number of VPPs N is 3. The price of electricity sold by IESP to the grid and the price of electricity purchased from the grid are as follows: Figure 3 shown. is the unit operating cost of pumped storage, which is 0.28 yuan / kWh in this embodiment. is the start-up and shutdown fee of the pumped storage unit, which is 500 yuan per time in this embodiment. , , They represent the operating costs of batteries, hydrogen energy storage, and compressed air energy storage per unit power, respectively. In this embodiment, they are 0.23 yuan / kWh, 0.25 yuan / kWh, and 0.35 yuan / kWh, respectively. represents the operating cost per unit power of photovoltaic power generation, which is 0.25 yuan / kWh in this embodiment. represents the operating cost of wind power generation per unit power, which is 0.2 yuan / kWh in this embodiment. It represents the operating cost of thermal power generation per unit power, and in this embodiment is set to 0.4 yuan / kWh. is the maximum value of the difference in reservoir capacity between the beginning and end of the day. In this example, it is taken as 8 million cubic meters. is the number of generators in the hydropower station, which is 2 in this embodiment. and They represent the maximum and minimum values ​​of the upper reservoir capacity respectively. In this embodiment, they are 20 million cubic meters and 12 million cubic meters respectively. The maximum percentage of remaining battery power. are the minimum percentages of remaining battery power that can be achieved. In this embodiment, the values ​​are 90% and 10% respectively. is the volume of the gas storage tank, which is 10000m3 in this embodiment. 3 . is the carbon quota coefficient, which is 0.45tCO2 / MWh in this embodiment. is the carbon emission coefficient per unit of electricity of the coal-fired unit, and in this embodiment, the value is 0.638tCO2 / MWh. base is the benchmark carbon price, which is 89.32 yuan / ton in this example. is the permeability sensitivity coefficient, which reduces the carbon price in real time in the high-energy renewable energy scenario. In this example, it is set to 0.85. d0 is the baseline interval length. In this example, it is set to 5. α0 is the initial increase. In this example, it is set to 0.9. In this example, fixed loads account for 80% of the total load, and movable loads account for 20%. Indicates the price response elasticity coefficient, which is 0.85 in this embodiment. In this embodiment, the energy storage SOE adjustment cost curve is as follows: Figure 4 shown. Figure 5 is the optimal dispatching scheme of VPP1, where A1 is thermal power output, A2 is wind power output, A3 is photovoltaic output, A4 is pumped storage discharge, A5 is pumped storage charging, A6 is compressed air energy storage discharge, A7 is compressed air energy storage charging, A8 is purchasing electricity from IESP, and A9 is selling electricity to IESP.

[0122] Figure 6 is the optimal dispatching scheme of VPP2, where B1 is thermal power output, B2 is wind power output, B3 is photovoltaic output, B4 is hydrogen energy storage discharge, B5 is hydrogen energy storage charging, B6 is electrochemical energy storage discharge, B7 is electrochemical energy storage charging, B8 is purchasing electricity from IESP, and B9 is selling electricity to IESP.

[0123] Figure 7 is the optimal dispatching scheme of VPP3, where C1 is thermal power output, C2 is wind power output, C3 is electrochemical energy storage discharge, C4 is electrochemical energy storage charging, C5 is electricity purchase from IESP, and C6 is electricity sale to IESP.

[0124] Figure 8 is the optimal dispatching scheme of IESP, where D1 is the on-grid power, D2 is the power purchased from the grid, D3 is the power sold to VPP1, D4 is the power purchased from VPP1, D5 is the power sold to VPP2, D6 is the power purchased from VPP2, D7 is the power sold to VPP3, and D8 is the power purchased from VPP3.

[0125] Actual case verification shows that this method successfully achieved the dual goals of increasing economic benefits and reducing carbon emissions in a power grid in a certain region of a province, providing strong technical support for the low-carbon and market-oriented operation of the new power system, and has important practical significance for promoting the "dual carbon" strategy.

[0126] Therefore, the present invention has the following advantages: 1. A master-slave game framework is used to achieve dynamic collaborative optimization of the carbon-electricity-storage multi-market coupling. By building a two-tier game model between integrated energy service providers at the upper level and virtual power plants at the lower level, economic benefits and low-carbon goals are effectively coordinated, alleviating the conflict between economic benefits and carbon emission constraints in traditional markets. 2. Introducing a tiered carbon trading mechanism and demand response technology to directly incorporate carbon emission costs into power market optimization objectives. Simultaneously, leveraging the transferability and curtailment of loads, this will significantly improve the efficiency of renewable energy consumption and reduce the system's curtailment of wind and solar power. 3. Adopting a flexible energy storage state operation mode and dynamically adjusting the energy storage system's market participation strategy to break through the limitations of the traditional time-of-use bidding mechanism, fully tap the peak-shaving and valley-filling potential of energy storage, and enhance its flexibility and competitiveness in the power market; 4. It can achieve high-precision wind and solar power output forecasts. By decomposing signal modalities and extracting deep time series features, it can effectively smooth out the volatility of renewable energy output and provide reliable data support for scheduling optimization. 5. Build an iterative optimization algorithm for a two-layer dynamic game. Through a two-way feedback mechanism between electricity prices and dispatch decisions, it quickly converges to a Stackelberg equilibrium solution, ensuring the global optimal returns of market participants and reducing supply and demand deviations and operational risks.

[0127] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the embodiments disclosed herein. The present invention is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art not disclosed herein. It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and variations can be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for optimizing and scheduling an integrated energy system, characterized in that: include: Build an integrated energy system architecture when new energy and energy storage are connected to the power system; The integrated energy system architecture includes a power grid, an integrated energy service provider and at least one virtual power plant; Obtaining new energy output forecast results and building an energy storage model; based on the new energy output forecast results and the energy storage model, establishing a collaborative optimization scheduling model between the upper-level integrated energy service provider and the lower-level virtual power plant, determining the objective function, and setting constraints; Embed the carbon trading market into the integrated energy system architecture using a tiered carbon trading model, taking into account the transferability and curtailment of loads, and adopting demand response where loads are concentrated; With integrated energy service providers as leaders setting electricity prices and virtual power plants as followers responding to dispatch, a Stackelberg game model with one master and multiple followers is established. It is proven that the Stackelberg game model has a unique equilibrium solution, achieving the optimal synergy between the global economy and low-carbon goals. The KKT condition is used to transform the lower-level virtual power plant optimization problem into the upper-level integrated energy service provider constraints. A single-level linear programming model is constructed and solved using the CPLEX solver to output the power system scheduling strategy.

2. The integrated energy system optimization scheduling method according to claim 1, characterized in that: The new energy output forecast results include wind and solar power forecast values; The obtaining of the new energy output prediction result includes: The variational mode decomposition algorithm is used to perform variational mode decomposition on the historical wind and solar power, decomposing the complex signal into multiple sparse intrinsic mode function components. A CNN-BiLSTM model is constructed, and the intrinsic mode function components are input as a combination of wind direction angle, wind speed, and temperature. The Adam optimizer is used to train the CNN-BiLSTM model. After training, the predicted intrinsic mode function components are superimposed to obtain the predicted wind and solar power value.

3. The integrated energy system optimization scheduling method according to claim 1, characterized in that: The energy storage includes pumped storage power stations, electrochemical energy storage, hydrogen energy storage and compressed air energy storage.

4. The integrated energy system optimization scheduling method according to claim 3, characterized in that: The objective function includes maximizing the revenue of the upper-level integrated energy service provider and minimizing the cost of the lower-level virtual power plant, and the expression is: Where, F IESP is the income of the upper-level integrated energy service provider; T is the total number of time periods; N is the number of virtual power plants; The income from electricity sales by the integrated energy service provider to the i-th virtual power plant in time period t; The cost of electricity purchased by the integrated energy service provider from the i-th virtual power plant in time period t; is the income from electricity sales by the integrated energy service provider to the grid in time period t, The cost of electricity purchased from the grid by the integrated energy service provider in time period t; in, Where, and They represent the electricity sales and purchase prices of the integrated energy service provider to the i-th virtual power plant in the t-th time period, and They represent the power sold and purchased by the integrated energy service provider to the i-th virtual power plant in the t-th time period, and They represent the electricity sales and purchase prices of the integrated energy service provider to the power grid in time period t, and They represent the power sold and purchased by the integrated energy service provider to the grid in time period t respectively; Where, is the cost of the lower-level virtual power plant; is the cost of purchasing electricity from the integrated energy service provider in time period t; is the electrochemical energy storage operating cost in time period t; is the hydrogen storage cost in time period t; is the operating cost of the pumped storage power station in time period t; is the compressed air energy storage operating cost in time period t; is the photovoltaic power generation cost in the tth time period; is the wind power generation cost in the tth time period; is the thermal power generation cost in the tth time period; is the electricity sales revenue to the integrated energy service provider in time period t; in, Where, and are the electricity purchase and electricity sales prices of the integrated energy service provider in time period t, and are the electricity purchase and sales power of the integrated energy service provider in time period t respectively.

5. The integrated energy system optimization scheduling method according to claim 4, characterized in that: The constraints include: Where, are the operating powers of photovoltaic power generation, wind power generation and thermal power generation in the tth time period respectively; , , , are the operating powers of the pumped storage power station, electrochemical energy storage, hydrogen energy storage and compressed air energy storage in the tth time period respectively; p Load,t is the power of the load in the tth time period.

6. The integrated energy system optimization scheduling method according to claim 5, characterized in that: The tiered carbon trading approach embeds the carbon trading market into the integrated energy system architecture, while taking into account the transferability and curtailment of loads, and adopting demand response where loads are concentrated, including: According to the preset standards, the carbon quota for thermal power generation is determined as follows: Where, Carbon quota for thermal power generation, is the carbon quota coefficient, is the operating power of the new energy unit in time period t; According to the preset standards, the carbon emission coefficient per unit of electricity generated by thermal power is determined, and the actual carbon emissions are calculated as follows: Where, is the actual carbon emissions from thermal power generation, is the carbon emission coefficient per unit of electricity generated by thermal power, is the unit time length; Then the actual carbon trading amount in the carbon trading market is obtained as follows: The dynamic parameter adjustment mechanism for carbon market transaction carbon prices is determined as follows: In the formula, c is the carbon price in carbon market, c base is the benchmark carbon price, is the permeability sensitivity coefficient; Where, d is the step interval length, d 0 is the length of the reference interval; Where, is the price increase factor, is the initial increase, is the reinforcing factor; Then the step-by-step carbon trading model is constructed as follows: Where, Carbon trading costs for the electricity system; When the user aggregator at the load aggregation location participates in demand response, the charge of the user aggregator is: Where, is the fixed load in time period t, is the translatable load in time period t; where the translatable load is: Where, It represents the basic amount of load that can be translated in the tth time period, represents the price response elasticity coefficient, represents the basic electricity purchase price in time period t, Indicates the maximum load that can be translated within T time periods.

7. The integrated energy system optimization scheduling method according to claim 6, characterized in that: The Stackelberg game model is: Among them, the integrated energy service provider and the virtual power plant are the two participants of the Stackelberg game model, and the participant set is represented as ; The strategic object of the leader integrated energy service provider is the purchase price and sales price of electricity per unit time, which is expressed in the form of a vector: The strategy objects of the follower virtual power plant are the output of thermal power generation, wind power generation, photovoltaic power generation and the energy storage strategy of pumped storage power station, electrochemical energy storage, hydrogen energy storage and compressed air energy storage, which are expressed as vectors ; The goal of integrated energy service providers is to maximize profits by optimizing electricity pricing strategies, while the goal of virtual power plants is to minimize costs by optimizing resource scheduling. In the Stackelberg game model, equilibrium is achieved when all followers make their own optimal decisions based on the leader's decision, and the leader also adjusts its own decision to the optimal level based on the follower's decision feedback. represents the equilibrium solution of the master-slave game in the Stackelberg game model, then: In the equilibrium solution, any participant cannot increase its profit by unilaterally adjusting its strategy.

8. The integrated energy system optimization scheduling method according to claim 7, characterized in that: The conditions that must be met for the Stackelberg game model to have a unique equilibrium solution are: The strategy sets of the leader and the follower are nonempty, compact, convex sets; When the leader's strategy is determined, all followers have a unique optimal solution; When the leader's strategy is determined, there is a unique optimal solution for the leader.

9. The integrated energy system optimization scheduling method according to claim 7, characterized in that: The KKT condition is used to transform the lower-level virtual power plant optimization problem into the upper-level integrated energy service provider constraint, construct a single-level linear programming model, use the CPLEX solver to solve it, and output the power system scheduling strategy, including: The cost function of the virtual power plant is transformed into an augmented Lagrangian function, and the KKT equilibrium condition and complementary relaxation condition are set. The CPLEX solver is used to maximize the profit function of the upper-level integrated energy service provider. Its constraints include not only the main constraints of the upper-level planning and the constraints of the lower-level virtual power plant, but also the complementary relaxation conditions of the lower-level optimization problem are transformed into equivalent algebraic constraints through KKT condition transformation, and the coordinated optimization of the two-level decision-making is realized simultaneously, and the power system dispatching strategy that meets the global optimality requirements is output.

10. An integrated energy system optimization and scheduling device, characterized in that: include: A construction unit for building an integrated energy system architecture when new energy and energy storage are connected to the power system; The integrated energy system architecture includes a power grid, an integrated energy service provider and at least one virtual power plant; Establish a unit for obtaining new energy output forecast results and building an energy storage model. Based on the new energy output forecast results and the energy storage model, establish a collaborative optimization scheduling model for the upper-level integrated energy service provider and the lower-level virtual power plant, determine the objective function, and set constraints; a response unit, configured to embed the carbon trading market into the integrated energy system architecture by adopting a tiered carbon trading system, while taking into account the transferability and curtailment of loads and adopting demand response at load aggregation locations; An optimization unit is used to establish a one-master, multiple-slave Stackelberg game model with integrated energy service providers as leaders setting electricity prices and virtual power plants as followers responding to dispatch requests. The model also proves that a unique equilibrium solution exists, achieving optimal coordination between the global economy and low-carbon goals. The output unit is used to transform the lower-level virtual power plant optimization problem into the upper-level integrated energy service provider constraints using KKT conditions, construct a single-level linear programming model, solve it using the CPLEX solver, and output the power system scheduling strategy.

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